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

Chapter 1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks

This chapter focuses on technical solutions to set up popular deep learning frameworks. First, we provide solutions to set up a stable and flexible environment on local machines and with cloud solutions. Next, all popular Python deep learning frameworks are discussed in detail:


  • Setting up a deep learning environment
  • Launching an on Amazon Web Services (AWS)
  • Launching an on Google Cloud Platform (GCP)
  • Installing CUDA and cuDNN
  • Installing Anaconda and libraries
  • Connecting with Jupyter Notebook on a server
  • Building state-of-the-art, production-ready models with TensorFlow
  • Intuitively building networks with Keras
  • Using PyTorch's dynamic computation graphs for RNNs
  • Implementing high-performance models with CNTK
  • Building efficient models with MXNet
  • Defining networks using simple and efficient code with Gluon