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

Questions and answers

  1. Was there a significant difference in performance between a wide or deep network?

Not much in the case, we studied here. However, one thing you must remember is that both networks learned fundamentally different things or aspects of the input. Therefore, in other applications, the performance might vary.

  1. Is deep learning the same as a deep neural network?

No. Deep learning is the area of machine learning focused on all algorithms that train over-parametrized models using novel gradient descent techniques. Deep neural networks are networks with many hidden layers. Therefore, a deep network is deep learning. But deep learning is not uniquely specific to deep networks.

  1. Could you give an example of when sparse networks are desired?

Let's think about robotics. In this field, most things run on microchips that have memory constraints and storage constraints and computational power constraints; finding neural architectures whose weights are mostly zero would...