Karim Md. R. Predictive Analytics with TensorFlow

Pckt Publishing, 2017. — 552 p. — ISBN: 1788398920.Harness the power of data in your business by building advanced predictive modelling applications with Tensorflow.A quick guide to gaining hands-on experience with deep learning in different domains such as digit, image & text classification
Build your own smart, predictive models with TensorFlow using an easy-to-follow approach
Understand deep learning and predictive analytics along with its challenges and best practicesWho This Book Is ForThis book isfor anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need!What You Will LearnGain a solid theoretical understanding of linear algebra, statistics, and probability for predictive modeling
Develop predictive models using classification, regression, and clustering algorithms
Develop predictive models for NLP
Learn how to use reinforcement learning for predictive analytics
Factorization Machines for advanced recommendation systems
Get a hands-on understanding of deep learning architectures for advanced predictive analytics
Learn how to use deep and recurrent Neural Networks for predictive analytics
Explore Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysisIn Detail
Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence.This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling.The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system.The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis.