Book description
Skip the theory and get the most out of Tensorflow to build productionready machine learning models
Key Features
 Exploit the features of Tensorflow to build and deploy machine learning models
 Train neural networks to tackle realworld problems in Computer Vision and NLP
 Handy techniques to write productionready code for your Tensorflow models
Book Description
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before.
With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through realworld examples, you will get handson experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.
By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in realworld scenarios.
What you will learn
 Become familiar with the basic features of the TensorFlow library
 Get to know Linear Regression techniques with TensorFlow
 Learn SVMs with handson recipes
 Implement neural networks to improve predictive modeling
 Apply NLP and sentiment analysis to your data
 Master CNN and RNN through practical recipes
 Implement the gradient boosted random forest to predict housing prices
 Take TensorFlow into production
Who this book is for
If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. If you want to skip the theory and build productionready machine learning models using Tensorflow without reading pages and pages of material, this book is for you. Some background in Python programming is assumed.
Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files emailed directly to you.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 Dedication
 Packt Upsell
 Contributors
 Preface
 Getting Started with TensorFlow
 The TensorFlow Way

Linear Regression
 Introduction
 Using the matrix inverse method
 Implementing a decomposition method
 Learning the TensorFlow way of linear regression
 Understanding loss functions in linear regression
 Implementing deming regression
 Implementing lasso and ridge regression
 Implementing elastic net regression
 Implementing logistic regression
 Support Vector Machines
 NearestNeighbor Methods
 Neural Networks
 Natural Language Processing
 Convolutional Neural Networks
 Recurrent Neural Networks
 Taking TensorFlow to Production
 More with TensorFlow
 Other Books You May Enjoy
Product information
 Title: TensorFlow Machine Learning Cookbook  Second Edition
 Author(s):
 Release date: August 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789131680
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