Book Image

Deep Learning for Beginners

By : Dr. Pablo Rivas
Book Image

Deep Learning for Beginners

By: Dr. Pablo Rivas

Overview of this book

With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.
Table of Contents (20 chapters)
1
Section 1: Getting Up to Speed
8
Section 2: Unsupervised Deep Learning
13
Section 3: Supervised Deep Learning

Introduction to recurrent neural networks

Recurrent neural networks (RNNs) are based on the early work of Rumelhart (Rumelhart, D. E., et al. (1986)), who was a psychologist who worked closely with Hinton, whom we have already mentioned here several times. The concept is simple, but revolutionary in the area of pattern recognition that uses sequences of data.

A sequence of data is any piece of data that has high correlation in either time or space. Examples include audio sequences and images.

The concept of recurrence in RNNs can be illustrated as shown in the following diagram. If you think of a dense layer of neural units, these can be stimulated using some input at different time steps, . Figures 13.1 (b) and (c) show an RNN with five time steps, . We can see in Figures 13.1 (b) and (c) how the input is accessible to the different time steps, but more importantly, the output of the neural units is also available to the next layer of neurons:

Figure 13.1. Different representations...