Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By : Sujit Pal, Nick McClure
Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By: Sujit Pal, Nick McClure

Overview of this book

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 real-world examples, you will get hands-on 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 real-world scenarios.
Table of Contents (13 chapters)

Working with a genetic algorithm

TensorFlow can also be used to update any iterative algorithm that we can express in a computational graph. One such iterative algorithm is the genetic algorithm, an optimization procedure.

Getting ready

In this recipe, we will illustrate how to implement a simple genetic algorithm. Genetic algorithms are a way to optimize over any parameter space (discrete, continuous, smooth, non-smooth, and so on). The idea is to create a population of randomly initialized solutions, and apply selection, recombination, and mutation to generate new (and potentially better) child solutions. The whole idea rests on the fact that we can calculate the fitness of an individual solution by seeing how well that...