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Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow.Key FeaturesUnderstand predictive analytics along with its challenges and best practicesEmbedded with assessments that will help you revise the concepts you have learned in this bookBook DescriptionPredictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim. What you will learnLearn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configurationExplore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analyticsSolve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analyticsDig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observationsLearn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasetsWho this book is forThis book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

One stop guide to implementing award-winning, and cutting-edge CNN architecturesKey Features[•]Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques[•]Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more[•]Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention modelsBook DescriptionConvolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.What you will learnFrom CNN basic building blocks to advanced concepts understand practical areas they can be applied toBuild an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize itLearn different algorithms that can be applied to Object Detection, and Instance SegmentationLearn advanced concepts like attention mechanisms for CNN to improve prediction accuracyUnderstand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and moreUnderstand the working of generative adversarial networks and how it can create new, unseen imagesWho this book is forThis book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4jSolve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster wayCover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework.Book DescriptionMachine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.What you will learnApply advanced regression techniques to boost the performance of predictive modelsUse different classification algorithms for business analyticsGenerate trading strategies for Bitcoin and stock trading using ensemble techniquesTrain Deep Neural Networks (DNN) using H2O and Spark MLUtilize NLP to build scalable machine learning modelsLearn how to apply reinforcement learning algorithms such as Q-learning for developing ML applicationLearn how to use autoencoders to develop a fraud detection applicationImplement LSTM and CNN models using DeepLearning4j and MXNetWho this book is forIf you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.