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Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will LearnTake advantage of the built-in support of the Google Colab ecosystemWork with TensorFlow data setsCreate input pipelines to feed state-of-the-art deep learning modelsCreate pipelined state-of-the-art deep learning models with clean and reliable Python codeLeverage pre-trained deep learning models to solve complex machine learning tasksCreate a simple environment to teach an intelligent agent to make automated decisionsWho This Book Is ForReaders who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab

Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.What You Will LearnBe familiar with the basic concepts and constructs of applied deep learningCreate machine learning models with clean and reliable Python codeWork with datasets common to deep learning applicationsPrepare data for TensorFlow consumptionTake advantage of Google Colab's built-in support for deep learningExecute deep learning experiments using a variety of neural network modelsBe able to mount Google Colab directly to your Google Drive accountVisualize training versus test performance to see model fitWho This Book Is ForReaders who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complexmachine learning algorithms.Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.What You'll LearnWork with simple and complex datasets common to Scikit-LearnManipulate data into vectors and matrices for algorithmic processingBecome familiar with the Anaconda distribution used in data scienceApply machine learning with Classifiers, Regressors, and Dimensionality ReductionTune algorithms and find the best algorithms for each datasetLoad data from and save to CSV, JSON, Numpy, and Pandas formatsWho This Book Is ForThe aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.

This book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others. The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications. By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing. What You Will Learn Be able to answer the question: What are Large Language Models? Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases Know the best practices for effective implementation Know the metrics and frameworks essential for evaluating the performance of Large Language Models Who This Book Is For An essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs

This comprehensive guide, featuring hand-picked examples of daily use cases, will walk you through the end-to-end predictive model-building cycle using the latest techniques and industry tricks. In Chapters 1, 2, and 3, we will begin by setting up the environment and covering the basics of PySpark, focusing on data manipulation. Chapter 4 delves into the art of variable selection, demonstrating various techniques available in PySpark. In Chapters 5, 6, and 7, we explore machine learning algorithms, their implementations, and fine-tuning techniques. Chapters 8 and 9 will guide you through machine learning pipelines and various methods to operationalize and serve models using Docker/API. Chapter 10 will demonstrate how to unlock the power of predictive models to create a meaningful impact on your business. Chapter 11 introduces some of the most widely used and powerful modeling frameworks to unlock real value from data. In this new edition, you will learn predictive modeling frameworks that can quantify customer lifetime values and estimate the return on your predictive modeling investments. This edition also includes methods to measure engagement and identify actionable populations for effective churn treatments. Additionally, a dedicated chapter on experimentation design has been added, covering steps to efficiently design, conduct, test, and measure the results of your models. All code examples have been updated to reflect the latest stable version of Spark. You will: Gain an overview of end-to-end predictive model building Understand multiple variable selection techniques and their implementations Learn how to operationalize models Perform data science experiments and learn useful tips

This book explores the integration of Generative AI within the Banking, Financial Services, and Insurance (BFSI) sector, elucidating its implications, applications, and the future landscape of BFSI. The first part delves into the origins and evolution of Generative AI, providing insights into its mechanics and applications within the BFSI context. It goes into the core technologies behind Generative AI, emphasizing their significance and practical applications. The second part explores how Generative AI intersects with core banking processes, ranging from transactional activities to customer support, credit assessment, and regulatory compliance. It focuses on the digital transformation driving investment banking into the future. It also discusses AI's role in algorithmic trading, client interactions, and regulatory adaptations. It analyzes AI-driven techniques in portfolio management, customer-centric solutions, and the next-generation approach to financial planning and advisory matters. The third part equips you with a structured roadmap for AI adoption in BFSI, highlighting the steps and the challenges. It outlines clear steps to assist BFSI institutions in incorporating Generative AI into their operations. It also raises awareness about the moral implications associated with AI in the BFSI sector. By the end of this book you will understand Generative AI's present and future role in the BFSI sector. What You Will Learn Know what Generative AI is and its applications in the BFSI sector Understand deep learning and its significance in generative models Analyze the AI-driven techniques in portfolio management and customer-centric solutions Know the future of investment banking and trading with AI Know the challenges of integrating AI into the BFSI sector Who This Book Is For Professionals in the BFSI and IT sectors, including system administrators and programmers

Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You'll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You'll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you'll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. What You'll Learn Understand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction Who This Book Is For Data Analysts, data scientists, Python programmers, and software developers new to data science.

This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks

Create interactive and data-driven dashboards using Python. This hands-on guide is a practical resource for those (with modest programming skills) in scientific and engineering fields looking to leverage Python's power for data visualization and analysis in a user-friendly dashboard format. You'll begin by gaining a fundamental understanding of Python programming, including data types, lists, dictionaries, and data structures. The book then delves into the world of reactive programming with Plotly and Dash, offering a hands-on approach to building interactive web-based dashboards. Next, you'll see how to work with online data, how to scrape and clean datasets, and keep files up-to-date. The book also guides you through planning a dashboard prototype, outlining project tasks, trends, forecasts, spectra, and other design considerations. It concludes with a discussion of how the dashboard can be used for data visualization of real data, explaining the usefulness of tools such as spectra. By providing detailed examples for download and customization, Prototyping Python Dashboards for Scientists and Engineers will equip you with the skills needed to jumpstart your own development efforts. What You'll Learn Design a dashboard with PythonConvert and filter Excel formatted files to produce CSV filesCreate browser-served graphics with PLOTLYGenerate polynomial trend lines for forecastingBuild a Unix service to share your dashboard Who This Book Is For Scientists, engineers, students, programmers, and data enthusiasts who aspire to harness Python's potential for data visualization and analysis through the creation of interactive dashboards. Many will be pragmatic programmers with modest skills and limited resources who mainly want to see a working solution they could emulate.

Did you ever think that the way you interact with other human beings, entities, and organizations can be measured for building a harmonious society? In this age of user verification and user validation, AI will fine-tune these interactions and transactions to build a socially equitable world. This book reveals that social equity is not only what you get from society, but also what you give to it. You'll explore the concepts and intricacies of this mutually built phenomenon. The book then and focuses on creating social equity awareness and details how AI can help in the process. Regular enterprises, consumer-oriented companies, and social media companies all capture data in regular interactions and use this information in various ways. You'll learn how this information is being leveraged and explore how social equity can thrive when all this information is analyzed and measured.Building Social Equity with AI highlights the subtle nuances of social equity and howAI can create more awareness towards it, thus improving your business or organization.What You'll LearnUnderstand how social equity affects all business and industry professionalsBuild social equity into a changing worldSee how AI and GenAI stand out as transformative forcesIntegrate social equity into network structuresAddress equality and equity in the context of people, processes, and technologyWork with AI to improve social equity scores Who This Book Is ForSmall-medium business owners and users, consultants, education specialists, career coaches, data engineers, and social workers who want to leverage AI to gain social equity for either their business or personal life.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You'll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You'll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you'll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language.

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll Learn Gain an understanding of the MLOps discipline Know the MLOps technical stack and its components Get familiar with the MLOps adoption strategy Understand feature engineering Who This Book Is For Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production

Develop and implement AI strategies aligned with business goals, including operating models and partnership strategies. This book is practical guide for chief experience officers and other corporate board members faced with the complex issues of AI governance, data privacy, AI regulations, AI copyright, AI strategy, and more. Executives are eager to hear from other executives, peers, and authority figures regarding AI matters, and how to approach them quickly, correctly and meaningfully. The cost of missing out or messing up in AI transformation is easy so it's imperative that C-suite and board members have the right mental framework to ask the right questions for their organizations. Throughout this book, you'll see how to develop and execute AI strategies that align with your organizational goals and ethical standards. You'll navigate the complex landscape of AI regulation and governance, applying best practices to ensure compliance and protect stakeholder interests. You'll also, understand how to innovate and adapt AI technologies within your operations. AI and the Boardroom provides all the right tools to guide decision-making, foster partnerships, and enhance customer experiences What You Will Learn Master AI governance, regulations, and ethical considerations, including privacy and intellectual property issues. Optimize AI investments, budgets, and ROI through effective KPIs, OKRs, and risk management. Navigate organizational changes brought by AI, including executive compensation, team structures, and change management. Leverage AI for board-level decision-making while advancing organizational AI maturity and staying ahead of emerging trends. Who This Book Is For Suite executives and corporate board members; technology and innovation leaders; risk management and compliance professionals; corporate strategists and business unit leaders; AI program managers and data scientists

As artificial intelligence (AI) rapidly transforms education, tools like ChatGPT and Claude are revolutionizing the way we teach and learn. This book is a groundbreaking book that empowers parents and students to navigate this exciting new frontier, filling a critical gap in the current literature. As the first comprehensive guide to generative AI in education designed for parents and students, Generative AI in Education is positioned to become an indispensable resource. It provides the knowledge and strategies needed to effectively integrate AI into their learning journeys, transforming educational outcomes and preparing students for success in a rapidly changing world. You'll gain a deep understanding of how tools like ChatGPT and Claude work, and how they can be leveraged to support learning across various subjects and grade levels. You'll then see how to create clear, specific, and engaging prompts that elicit valuable responses from AI-powered tools. This book contains all the techniques for tailoring prompts to different learning objectives, styles, and contexts, and how they can use AI tools to support reading comprehension, writing skills, problem-solving, and creative thinking. What You Will Learn Apply generative AI in education Craft effective prompts for personalized learning experiences Utilize AI tools to support learning, creativity, and problem-solving Who This Book is For Parents and students who are eager to harness the power of generative AI to enhance learning experiences and prepare for success in an AI-driven future

Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today's computational world. This book is an introductory guide to NLP and LLMs with Python programming. The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components. You'll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You'll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots. In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs. What You'll Learn Understand the basics of Python and the features of Python 3.11 Explore the essentials of NLP and how do they lay the foundations for LLMs. Review LLM components. Develop basic apps using LLMs and Python. Who This Book Is For Data analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

This book will help you comprehend the impact of artificial intelligence (AI) on information security, data privacy, and data security.The book starts by explaining the basics and setting the goals for a complete understanding of how AI, Information Security, Data Privacy, and Data Security all connect. Then, it gives you important information about the basics of AI, machine learning, and deep learning in simple terms. It also talks about the ethics of using AI in privacy and security, making sure you understand the power and responsibility that come with AI. Next, it takes you through the complex world of information security and data privacy. It covers everything from the current state of security to how AI can detect threats and protect privacy. Additionally, it delves into ethical considerations to ensure the responsible use of AI in managing data privacy. Later chapters discuss strategies and future trends in using AI for data security, finding the right balance between security and privacy, and giving useful advice for organizations.In the end, this book examines the current landscape and foresees the future, underscoring the vital importance of maintaining a balance between innovation and privacy in AI-powered security.What you will learn:How AI is being used to detect and prevent cyberattacks in real-timeWhat are the AI-powered techniques for anonymizing and de-identifying data,What are the latest advancements in AI-powered privacy-enhancing technologies (PETs)How to find the right balance between security and privacyWho this book is for:Information security professionals, data scientists, and software developers seeking to gain an understanding of the latest trends and techniques in AI for information security

This comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent. The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practices By the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society. What You Will Learn Understand how Generative AI can be more energy-efficient through improvements such as model compression, efficient architecture, hardware optimization, and carbon footprint tracking Know the techniques to minimize data usage, including evaluation, filtering, synthesis, few-shot learning, and monitoring data demands over time Understand spanning efficiency, data minimization, and alignment for comprehensive responsibility Know the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairness Who This book Is For Professionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry settings; and researchers developing new generative AI techniques, such as at technology companies or universities

This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today's AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them? Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning

Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning covers how multiple agents compete, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO). You'll see how reinforcement learning with human feedback (RLHF) has been used by chatbots, built using Large Language Models, e.g. ChatGPT to improve conversational capabilities.You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which canbe run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it's for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.What You'll LearnExplore Python-based RL libraries, including StableBaselines3 and CleanRL Work with diverse RL environments like Gymnasium, Pybullet, and Unity MLUnderstand instruction finetuning of Large Language Models using RLHF and PPOStudy training and optimization techniques using HuggingFace, Weights and Biases, and Optuna Who This Book Is ForSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch.

This book is your comprehensive guide to building robust Generative AI solutions using the Databricks Data Intelligence Platform. Databricks is the fastest-growing data platform offering unified analytics and AI capabilities within a single governance framework, enabling organizations to streamline their data processing workflows, from ingestion to visualization. Additionally, Databricks provides features to train a high-quality large language model (LLM), whether you are looking for Retrieval-Augmented Generation (RAG) or fine-tuning. Databricks offers a scalable and efficient solution for processing large volumes of both structured and unstructured data, facilitating advanced analytics, machine learning, and real-time processing. In today's GenAI world, Databricks plays a crucial role in empowering organizations to extract value from their data effectively, driving innovation and gaining a competitive edge in the digital age. This book will not only help you master the Data Intelligence Platform but also help power your enterprise to the next level with a bespoke LLM unique to your organization. Beginning with foundational principles, the book starts with a platform overview and explores features and best practices for ingestion, transformation, and storage with Delta Lake. Advanced topics include leveraging Databricks SQL for querying and visualizing large datasets, ensuring data governance and security with Unity Catalog, and deploying machine learning and LLMs using Databricks MLflow for GenAI. Through practical examples, insights, and best practices, this book equips solution architects and data engineers with the knowledge to design and implement scalable data solutions, making it an indispensable resource for modern enterprises. Whether you are new to Databricks and trying to learn a new platform, a seasoned practitioner building data pipelines, data science models, or GenAI applications, or even an executive who wants to communicate the value of Databricks to customers, this book is for you. With its extensive feature and best practice deep dives, it also serves as an excellent reference guide if you are preparing for Databricks certification exams. What You Will Learn Foundational principles of Lakehouse architecture Key features including Unity Catalog, Databricks SQL (DBSQL), and Delta Live Tables Databricks Intelligence Platform and key functionalities Building and deploying GenAI Applications from data ingestion to model serving Databricks pricing, platform security, DBRX, and many more topics Who This Book Is For Solution architects, data engineers, data scientists, Databricks practitioners, and anyone who wants to deploy their Gen AI solutions with the Data Intelligence Platform. This is also a handbook for senior execs who need to communicate the value of Databricks to customers. People who are new to the Databricks Platform and want comprehensive insights will find the book accessible.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution.This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.What You Will LearnWrite code for machine learning, adaptive control, and estimation using MATLABUse MATLAB graphics and visualization tools for machine learningBecome familiar with neural netsBuild expert systemsUnderstand adaptive controlGain knowledge of Kalman FiltersWho This Book Is ForSoftware engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.

This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms. The book includes a thorough examination of machine learning algorithms that find application in both NTMA and VQA, with a specific emphasis on classification and prediction algorithms such as the Multi-Layer Perceptron and Support Vector Machine. The book also explores the software architecture of the NTMA client-server application. This architecture is meticulously developed using HTML, CSS, Node.js, and JavaScript. Practical aspects of developing the Video Quality Assessment (VQA) model using JavaScript and Java are presented. Lastly, the book provides detailed guidance on implementing a complete system model that seamlessly merges NTMA and VQA into a unified web application, all built upon a client-server paradigm. By the end of the book, you will understand NTMA and VQA concepts and will be able to apply machine learning to both domains and develop and deploy your own NTMA and VQA applications using JavaScript and Node.js. What You Will Learn What are the fundamental concepts, existing applications, and research on NTMA? What are the existing software and current research trends in VQA? Which machine learning algorithms are used in NTMA and VQA? How do you develop NTMA and VQA web-based applications using JavaScript, HTML, and Node.js? Who This Book Is For Software professionals and machine learning engineers involved in the fields of networking and telecommunications

Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production.The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation). These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included.By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business.What You Will LearnWhat is Generative AI?What is ChatGPT and GPT4?What are language models and diffusions models?How do we deploy LangChain and HuggingFace?Who This Book Is ForSoftware engineers with a few years of experience building applications in any language or infrastructure

This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is appliedGrasp the core concepts of machine learning.Master traditional machine learning approaches to anomaly detection using scikit-kearn.Understand deep learning in Python using Keras and PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is For Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists

Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students. What You Will Learn Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approachesModel problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learningUtilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methodsUnderstand the architecture and advantages of distributed reinforcement learningMaster the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agentsExplore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is For Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.

Deep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain – tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data – an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs – Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks – through both their ‘default'usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind – a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems.What You Will LearnImportant concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications.Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn't appropriate.Apply promising research and unique modeling approaches in real-world data contexts.Explore and engage with modern, research-backed theoretical advances on deep tabular modelingUtilize unique and successful preprocessing methods to prepare tabular data for successful modelling. Who This Book Is ForData scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security.

This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions. Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need? He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, you'll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user's perspective. You'll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup. After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems. What You Will Learn Understand the challenges associated with implementing a data lakeExplore the architectural patterns and processes used to design a new data lakeDesign and implement data lake capabilitiesAssociate business requirements with technical deliverables to drive success Who This Book Is For Data Scientists and Architects, Machine Learning Engineers, and Software Engineers.

Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.The book kicks off with an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biasescan be eliminated by combining machine and human intelligence.Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.What You Will LearnImplement full-fledged decision intelligence applications using PythonLeverage the tools, techniques, and methodologies for prescriptive AIUnderstand how prescriptive AI can be used in different domains through practical examplesInterpret results and integrate them into your decision makingWho This Book Is ForData Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business.

This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark's structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows. This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming's execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and notebooks hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use.Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark.What You Will LearnMaster the concepts of Spark clusters and batch data processingUnderstand data ingestion, transformation, and data storageGain insight into essential stream processing concepts and different streaming architecturesImplement streaming jobs and applications with Spark StreamingWho This Book Is ForData engineers, data analysts, machine learning engineers, Python and R programmers

Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. What You'll LearnSee how data science works in the SEO contextThink about SEO challenges in a data driven wayApply the range of data science techniques to solve SEO issuesUnderstand site migration and relaunches areWho This Book Is ForSEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application.What You Will LearnUnderstand the fundamental concepts of working with geodataWork with multiple geographical data types and file formats in PythonCreate maps in PythonApply machine learning on geographical data Who This Book Is ForReaders with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment

Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will Learn Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learn Who This Book Is For Data scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.

This book is aimed at practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It will guide you through the fundamentals of technical decision making, including planning, building, optimizing, packaging, and deploying end-to-end, reliable, and robust stochastic workflows using the language of data science. MLOps Lifecycle Toolkit walks you through the principles of software engineering, assuming no prior experience. It addresses the perennial “why” of MLOps early, along with insight into the unique challenges of engineering stochastic systems. Next, you'll discover resources to learn software craftsmanship, data-driven testing frameworks, and computer science. Additionally, you will see how to transition from Jupyter notebooks to code editors, and leverage infrastructure and cloud services to take control of the entire machine learning lifecycle. You'll gain insight into the technical and architectural decisions you're likely to encounter, as well as best practices for deploying accurate, extensible, scalable, and reliable models. Through hands-on labs, you will build your own MLOps “toolkit” that you can use to accelerate your own projects. In later chapters, author Dayne Sorvisto takes a thoughtful, bottom-up approach to machine learning engineering by considering the hard problems unique to industries such as high finance, energy, healthcare, and tech as case studies, along with the ethical and technical constraints that shape decision making. After reading this book, whether you are a data scientist, product manager, or industry decision maker, you will be equipped to deploy models to production, understand the nuances of MLOps in the domain language of your industry, and have the resources for continuous delivery and learning. What You Will Learn Understand the principles of software engineering and MLOpsDesign an end-to-endmachine learning systemBalance technical decisions and architectural trade-offsGain insight into the fundamental problems unique to each industry and how to solve them Who This Book Is For Data scientists, machine learning engineers, and software professionals.

This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You'll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you'll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare.What You Will LearnUnderstand the components required to facilitate precision health and personalized careApply and implement precision health systemsOvercome the challenges of delivering precision healthcare at scaleReconcile ethical and moral implications of delivering precision healthcareGain insight into the hurdles providers face while implementing precision healthcare Who This Book Is ForHealthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students

If you're a complete newbie who's wondering exactly what ChatGPT is, what is does, and how it can be a valuable resource for non-coders, this is the book for you. With a comprehensive exploration of ChatGPT's features, foundations, and applications, this guide will serve as a valuable resource for beginners venturing into the world of conversational AI. ChatGPT for Beginners has four parts. Part one provides a step-by-step introduction to using ChatGPT, from accessing the OpenAI website and creating an account to starting conversations, changing responses, and getting conversation summaries. Part two delves into the foundations of the large language model that powers ChatGPT. It covers topics such as the definition of ChatGPT, its knowledge domains, the basics of natural language processing, machine learning techniques applied to language processing, and the role of neural networks in ChatGPT's operation. Part three covers a wide range of practical applications,from letter writing to business content creation, text translation, language learning, recruitment processes, artistic content creation, and fostering innovation and creativity. Part four examines the strengths and limitations of ChatGPT, addressing ethical considerations related to data security, bias, and partiality. It also delves into the future advancements and challenges that lie ahead for ChatGPT, providing insights into the developing landscape of conversational AI. After completing this book, you will be able to harness the full potential of ChatGPT. Whether you are a student, professional, or are just curious about the capabilities of this AI technology, this book will serve as your essential companion in unlocking the possibilities of ChatGPT. What You Will Learn Access ChatGPT, create an account, and initiate conversations Understand what ChatGPT is, its knowledge domains, and how it works through natural language processing, machine learning, and neural networks Ask relevant questions to ChatGPT, and obtain quality responses Manage the degree of randomness in the responses using parameters such as temperature, frequency, and presence Who This Book Is For Students, professionals, and anyone else curious about the capabilities of ChatGPT.

This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust.The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will LearnBuild AI/ML models using Responsible AI frameworks and processesDocument information on your datasets and improve data qualityMeasure fairness metrics in ML modelsIdentify harms and risks per task and run safety evaluations on ML modelsCreate transparent AI/ML modelsDevelop Responsible AI principles and organizational guidelinesWho This Book Is ForAI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms

This book presents the process and framework you need to transform aspects of our world into data that can be collected, analyzed, and used to make decisions. You will understand the technologies used to gather and process data from many sources, and you will learn how to analyze data with AI and ML models.Datafication is becoming increasingly prevalent in many areas of our lives, from business to education and healthcare. It has the potential to improve decision-making by providing insights into patterns, trends, and correlation between seemingly unconnected pieces of data. This book explains the evolution, principles, and patterns of datafication used in our day-to-day activities. It covers how to collect data from a variety of sources, using technologies such as edge, streaming techniques, REST, and frameworks, as well as data cleansing and data lineage. A data analysis framework is provided to guide you in designing and developing AI and ML projects, including the details of sentiment and behavioral analytics.Introduction to Datafication teaches you how to engineer AI and ML projects by using various methodologies, covers the security mechanisms to be applied for datafication, and shows you how to govern the datafication process with a well-defined governance framework.What You Will LearnUnderstand the principles and patterns to be adopted for dataficationGain techniques for sourcing and mining data, and for sharing data with a data pipelineLeverage the AI and ML algorithms most suitable for dataficationUnderstand the data analysis framework used in every AI and ML projectMaster the details of sentiment and behavioral analytics through practical examplesUtilize development methodologies for datafication engineering and the related security and governance framework Who This Book Is ForStudents, data scientists, data analysts, and AI and ML engineers

Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings.Machine Learning for Auditors provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization.What You Will LearnUnderstand the role of auditors as trusted advisorsPerform exploratory data analysis to gain a deeper understanding of your organizationBuild machine learning predictive models that detect fraudulent vendor payments and expensesIntegrate data analytics with existing and new technologiesLeverage storytelling to communicate and validate your findings effectivelyApply practical implementation use cases within your organizationWho This Book Is ForAI Auditing is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes.

Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images. The topics covered in this book not only encompass areas of the medical field where AI is already playing a major role, but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to those problems. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients.

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you'll jump into simple classification programs for hand-writing analysis. Once you've tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you'll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs andRNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! What You'll LearnDevelop using deep learning algorithmsBuild deep learning models using TensorFlow 2Create classification systems and other, practical deep learning applicationsWho This Book Is ForStudents, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. What You Will LearnUnderstand widespread supervised and unsupervised learning, including key dimension reduction techniquesKnow the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learningIntegrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworksDesign, build, test, and validate skilled machine models and deep learning modelsOptimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration Who This Book Is ForData scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics

Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That's where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect. Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You'll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You'll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications. After completing this book, you'll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making.What You Will LearnCreate synthetic tabular data with R and PythonUnderstand how synthetic data is important for artificial neural networksMaster the benefits and challenges of synthetic dataUnderstand concepts such as domain randomization and domain adaptation related to synthetic data generationWho This Book Is ForThose who want to learn about synthetic data and its applications, especially professionals working in the field of machine learning and computer vision. This book will also be useful for graduate and doctoral students interested in this subject.

Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects.This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks.All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be openeddirectly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.You will: •Understand the fundamental concepts of how neural networks work•Learn the fundamental ideas behind autoencoders and generative adversarial networks•Be able to try all the examples with complete code examples that you can expand for your own projects•Have available a complete online companion book with examples and tutorials.This book is for:Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticswith reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What You'll LearnBuild intelligent systems for enterpriseReview time series analysis, classifications, regression, and clusteringExplore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analyticsUnderstand Covers design patterns in Python Who This Book Is ForData scientists and software developers interested in the field of data analytics.

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You'll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you'll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. What You Will Learn Understand full-stack deep learning using TensorFlow 2.0Gain an understanding of the mathematical foundations of deep learning Deploy complex deep learning solutions in production using TensorFlow 2.0Understand generative adversarial networks, graph attention networks, and GraphSAGE Who This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. What You Will Learn How to start developing web applications using Streamlit What are Streamlit's components Media elements in Streamlit How to visualize data using various interactive and dynamic Python librariesHow to implement models in Streamlit web applicationsWho This Book Is ForProfessionals working in data science and machine learning domains who want to showcase and deploy their work in a web application with no prior knowledge of web development.

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigationAn aircraft that lands on Titan, the moon of Saturn, using reinforcement learningStock market predictionNatural language processingMusic creation usng generative deep learningPlasma controlEarth sensor processing for spacecraftMATLAB Bluetooth data acquisition applied to dance physics What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionNow includes reinforcement learningWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering. You'll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You'll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem. The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You'll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks. In the end, you'll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity. What You'll Learn Write fast and efficient code for data science and machine learningBuild robust and expressive data science pipelines Measure memory and CPU profile for machine learning methods Utilize the full potential of GPU for data science tasks Handle large and complex data sets efficiently Who This Book Is For Data scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.