(Reference Guide) Deep Learning for Beginners

Deep Learning for Beginners

This book aims to reach out to those beginners in deep learning who are looking for a strong foundation in the basic concepts required to build deep learning models using wellknown methodologies. If that sounds like you, then this book might be what you need. The book assumes no prior extensive exposure to neural networks and deep learning and starts by reviewing the machine learning fundamentals needed for deep learning. Then, it explains how to prepare data by cleaning and preprocessing it for deep learning and gradually goes on to introduce neural networks and the popular supervised neural network architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and unsupervised architectures, such as autoencoders (AEs), variational autoencoders (VAEs), and restricted Boltzmann machines (RBMs). At the end of each chapter, you will have a chance to test your understanding of the concepts and reflect on your own growth.