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


The recent advancements in deep learning can be, to some extent, attributed to the advancements in computing power. The increase in computing power, more specifically the use of GPUs for processing data, has contributed to the leap from shallow neural networks to deeper neural networks. In this chapter, we lay the groundwork for all following chapters by showing you how to set up stable environments for different deep learning frameworks used in this cookbook. There are many open source deep learning frameworks that are used by researchers and in the industry. Each framework has its own benefits and most of them are supported by some big tech company.

By following the steps in this first chapter carefully, you should be able to use local or cloud-based CPUs and GPUs to leverage the recipes in this book. For this book, we've used Jupyter Notebooks to execute all code blocks. These notebooks provide interactive feedback per code block in such a way that it's perfectly suited for storytelling.

The download links in this recipe are intended for an Ubuntu machine or server with a supported NVIDIA GPU. Please change the links and filenames accordingly if needed. You are free to use any other environment, package managers (for example, Docker containers), or versions if needed. However, additional steps may be required.