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

Setting up a deep learning environment

Before we get with training deep learning models, we need to set up our deep learning environment. While it is possible to run deep learning models on CPUs, the speed achieved with GPUs is significantly higher and necessary when running deeper and more complex models.

How to do it...

  1. First, you need to check whether you have to a CUDA-enabled NVIDIA GPU on your local machine. You can check the overview at
  2. If your GPU is listed on that page, you can continue installing CUDA and cuDNN if you haven't done that already. Follow the steps in the Installing CUDA and cuDNN section.
  1. If you don't have to an NVIDIA GPU on your local machine, you can decide to use a cloud solution. Follow the steps in the Launching a cloud solution section.