Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlow

Key Features

Understand the fundamental machine learning concepts useful in deep learning

Learn the underlying mathematical concepts as you implement deep learning models from scratch

Explore easy-to-understand examples and use cases that will help you build a solid foundation in DL

Book Description

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.

What you will learn

Implement recurrent neural networks (RNNs) and long short-term memory (LSTM) for image classification and natural language processing tasks

Explore the role of convolutional neural networks (CNNs) in computer vision and signal processing

Discover the ethical implications of deep learning modeling

Understand the mathematical terminology associated with deep learning

Code a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent space

Implement visualization techniques to compare AEs and VAEs

Who this book is for

This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.