Book Image

Hands-On Deep Learning with R

By : Michael Pawlus, Rodger Devine
Book Image

Hands-On Deep Learning with R

By: Michael Pawlus, Rodger Devine

Overview of this book

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.
Table of Contents (16 chapters)
1
Section 1: Deep Learning Basics
5
Section 2: Deep Learning Applications
12
Section 3: Reinforcement Learning

Comparing neural networks and the human brain

Let's consider how a human brain learns in order to see the ways in which a neural network is similar and the ways in which it is different.

Our brain contains a large number of neurons, and each neuron is connected to thousands of nearby neurons. As these neurons receive signals, they fire if the input contains a certain amount of a given color or a certain amount of a given texture. After millions of these interconnected neurons fire, the brain interprets the incoming signal as a certain class.

Of course, these connections are not set permanently but rather change dynamically as we continue to have experiences, notice patterns, and discover relationships. If we try a new fruit for the first time and discover that it is really sour, then all the attributes that help us recognize this fruit are connected with things that we know...