Karim Predictive Analytics with TensorFlow

1. Auflage 2017
ISBN: 978-1-78839-012-5
Verlag: Packt Publishing
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 522 Seiten

Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow.About This BookA quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and textsBuild your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the bookUnderstand deep learning and predictive analytics along with its challenges and best practicesWho This Book Is ForThis book is intended for 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 LearnGet a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modelingDevelop predictive models using classification, regression, and clustering algorithmsDevelop predictive models for NLPLearn how to use reinforcement learning for predictive analyticsFactorization Machines for advanced recommendation systemsGet a hands-on understanding of deep learning architectures for advanced predictive analyticsLearn how to use deep Neural Networks for predictive analyticsSee how to use recurrent Neural Networks for predictive analyticsConvolutional Neural Networks for emotion recognition, image classification, and sentiment analysisIn DetailPredictive 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.Style and approachTensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation.

Autoren/Hrsg.

Karim, Md. Rezaul