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 GANs and VAEs

In Chapter 9, Variational Autoencoders, we discussed VAEs as a mechanism for dimensionality reduction that aims to learn the parameters of the distribution of the input space, and effect reconstruction based on random draws from the latent space using the learned parameters. This offered a number of advantages we already discussed in Chapter 9, Variational Autoencoders, such as the following:

  • The ability to reduce the effect of noisy inputs, since it learns the distribution of the input, not the input itself
  • The ability to generate samples by simply querying the latent space

On the other hand, GANs can also be used to generate samples, like the VAE. However, the learning of both is quite different. In GANs, we can think of the model as having two major parts: a critic and a generator. In VAEs, we also have two networks: an encoder and a decoder.

If we were to make any connection between the two, it would be that the decoder and generator play a very similar...