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  • Book Overview & Buying Machine Learning Using TensorFlow Cookbook
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Machine Learning Using TensorFlow Cookbook

Machine Learning Using TensorFlow Cookbook

By : Alexia Audevart, Konrad Banachewicz, Luca Massaron
4.9 (16)
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Machine Learning Using TensorFlow Cookbook

Machine Learning Using TensorFlow Cookbook

4.9 (16)
By: Alexia Audevart, Konrad Banachewicz, Luca Massaron

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)
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5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

The TensorFlow Way

In Chapter 1, Getting Started with TensorFlow 2.x we introduced how TensorFlow creates tensors and uses variables. In this chapter, we'll introduce how to put together all these objects using eager execution, thus dynamically setting up a computational graph. From this, we can set up a simple classifier and see how well it performs.

Also, remember that the current and updated code from this book is available online on GitHub at https://github.com/PacktPublishing/Machine-Learning-Using-TensorFlow-Cookbook.

Over the course of this chapter, we'll introduce the key components of how TensorFlow operates. Then, we'll tie it together to create a simple classifier and evaluate the outcomes. By the end of the chapter, you should have learned about the following:

  • Operations using eager execution
  • Layering nested operations
  • Working with multiple layers
  • Implementing loss functions
  • Implementing backpropagation
  • Working with batch and stochastic training
  • Combining everything together

Let's start working our way through more and more complex recipes, demonstrating the TensorFlow way of handling and solving data problems.

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Machine Learning Using TensorFlow Cookbook
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