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


In this chapter, we will introduce a new type of neural network. Before, we've only considered neural networks where the information flows through the network in one direction. Next, we will introduce recurrent neural networks. In these networks, data is processed the same way for every element in a sequence, and the output depends on the previous computations. This structure has proven to be very for many applications, such as for natural language processing (NLP) and time series predictions. We will introduce the important building blocks that revolutionized how we process temporal or other forms of sequence data in neural networks.

A simple RNN unit is shown in Figure 4.1:

Figure 4.1: Example of the flow in an RNN unit

As we can see in the figure, the output of a RNN does not only depend on the current input Xt, but also on past inputs (Xt-1). Basically, this gives the network a type of memory. ;

There are multiple types of RNNs where the input and output dimension can differ...