Python Deep Learning Cookbook

Key Features

• Over 100 recipes on mathematical theory of each deep learning algorithm , its implementation and a bunch of related techniques for using them
• Provides explanation with examples covering deep learning algorithms using popular python frameworks like TensorFlow, Caffe, Keras, Theano
• Your ideal companion to train models involving neural networks problem and tuning it for a completely different problem, and getting impressive results.

Book Description

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, Caffe or Theano is provided. 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.

What you will learn

• Select the best Python framework for deep learning to use in case of specific problems/requirements
• Understand the definition of neural network models
• Learn to apply tips and tricks related to neural networks internals, to boost learning performances
• Consolidate machine learning principles and apply them in the deep learning field
• Reuse and adapt Python code snippets to everyday problems
• Evaluate the cost/benefits and performance implication of each discussed solution

Table of Contents

Chapter 1. Programming Environments, Gpu Computing, Cloud Solutions, And Deep Learning Frameworks
Chapter 2. Feed-Forward Neural Networks
Chapter 3. Convolutional Neural Networks
Chapter 4. Recurrent Neural Networks
Chapter 5. Reinforcement Learning
Chapter 6. Generative Adversarial Networks
Chapter 7. Computer Vision
Chapter 8. Natural Language Processing
Chapter 9. Speech Recognition And Video Analysis
Chapter 10. Time Series And Structured Data
Chapter 11. Game Playing Agents And Robotics
Chapter 12. Hyperparameter Selection, Tuning, And Neural Network Learning
Chapter 13. Network Internals
Chapter 14. Pretrained Models