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


During the last couple of years, a lot of game-changing network architectures have been proposed and published. Most of them open-sourced their code or published their weights. If the latter was not the case, others implemented the network architecture and shared the weights. As a result, many deep learning frameworks give direct access to popular models and their weights. In this chapter, we will demonstrate how to leverage these pretrained weights. Most of these models have been trained on large image datasets used in competitions, such as the ImageNet dataset. This has been published for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). By leveraging these pretrained weights, we can obtain good results and reduce training time.