Deep Learning for Beginners
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Section 1: Getting Up to Speed

Introduction to Machine Learning

Setup and Introduction to Deep Learning Frameworks

Preparing Data

Learning from Data

Training a Single Neuron

Training Multiple Layers of Neurons

Section 2: Unsupervised Deep Learning

Autoencoders

Deep Autoencoders

Variational Autoencoders

Restricted Boltzmann Machines

Section 3: Supervised Deep Learning

Deep and Wide Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Generative Adversarial Networks

Final Remarks on the Future of Deep Learning

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About 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 handson 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 wellversed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.
 Publication date:
 September 2020
 Publisher
 Packt
 Pages
 432
 ISBN
 9781838640859