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

Comparing a deep and shallow VAE on MNIST

Comparing shallow and deep models is part of the experimentation process that leads to finding the best models. In this comparison over MNIST images, we will be implementing the architecture shown in Figure 9.6 as the shallow model, while the deep model architecture is shown in Figure 9.7:

Figure 9.6 – VAE shallow architecture over MNIST

As you can appreciate, both models are substantially different when it comes to the number of layers involved in each one. The quality of the reconstruction will be different as a consequence:

Figure 9.7 – VAE deep architecture over MNIST

These models will be trained using a small number of epochs for the shallow VAE and a much larger number of epochs for the deeper model.

The code to reproduce the shallow encoder can be easily inferred from the example used in the Cleveland Heart Disease dataset; however, the code for the deep VAE will be discussed in the sections that follow.

Shallow VAE

One...