Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Genetic Algorithm (GA) to optimize hyperparameters

In all previous recipes, we've only static network architectures. More specifically, while training our network or agents the network didn't change. What we've also seen is that the network architecture and the hyperparameters can have a big affect on the results. However, often we don't know if a network will perform well or not in advance so we need to test it thoroughly. There are different ways to these hyperparameters. In Chapter 12, Hyperparameter Selection, Tuning, and Neural Network Learning, we demonstrate how to apply a grid search (with brute force) to find optimal hyperparameters. However, sometimes the hyperparameter space is enormous and using brute force will take too much time.

Evolutionary Algorithms (EA) have to be powerful. One of the most impressive outcomes is life. The optimization algorithms used in have been and are studied thoroughly. One of these is a Genetic Algorithm. This algorithm is inspired by life, it...