Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Introduction

In the previous chapter, we covered TensorFlow's fundamentals, and we are now able to set up a computational graph. This chapter will introduce Keras, a high-level neural network API written in Python with multiple backends. TensorFlow is one of them. François Chollet, a French software engineer and AI researcher currently working at Google, created Keras for his own personal use before it was open-sourced in 2015. Keras's primary goal is to provide an easy-to-use and accessible library to enable fast experiments.

TensorFlow v1 suffers from usability issues; in particular, a sprawling and sometimes confusing API. For example, TensorFlow v1 offers two high-level APIs:

  • The Estimator API (added in release 1.1) is used for training models on localhost or distributed environments
  • The Keras API was then added later (release 1.4.0) and intended to be used for fast prototyping

With TensorFlow v2, Keras became the...