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

Learning to play Tic-Tac-Toe

To show how adaptable neural networks can be, we will now attempt to use a neural network in order to learn the optimal moves for Tic-Tac-Toe. We will approach this knowing that Tic-Tac-Toe is a deterministic game and that the optimal moves are already known.

Getting ready

To train our model, we will use a list of board positions followed by the optimal response for a number of different boards. We can reduce the amount of boards to train on by considering only board positions that are different with regard to symmetry. The non-identity transformations of a Tic-Tac-Toe board are a rotation (in either direction) of 90 degrees, 180 degrees, and 270 degrees, a horizontal reflection, and a vertical reflection. Given this idea, we will use a shortlist of boards with the optimal move, apply two random transformations, and then feed that into our neural network for learning.

Since Tic-Tac-Toe is a deterministic game, it is worth noting that...