Download This PDF Book: MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence 1st ed. Edition by Phil Kim, for free.
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks.
In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.
With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.
Introduction
I was lucky enough to witness the world’s transition to an information society, followed by a networked environment. I have been living with the changes since I was young. The personal computer opened the door to the world of information, followed by online communication that connected computers via the Internet, and smartphones that connected people.
Now, everyone perceives the beginning of the overwhelming wave of artificial intelligence. More and more intelligent services are being introduced, bringing in a new era. Deep Learning is the technology that led this wave of intelligence. While it may hand over its throne to other technologies eventually, it stands for now as a cornerstone of this new technology.
Deep Learning is so popular that you can find materials about it virtually anywhere. However, not many of these materials are beginner friendly. I wrote this book hoping that readers can study this subject without the kind of difficulty
I experienced when first studying Deep Learning. I also hope that the step-bystep approach of this book can help you avoid the confusion that I faced.
This book is written for two kinds of readers. The first type of reader is one who plans to study Deep Learning in a systematic approach for further research and development. This reader should read all the content from the beginning to end.
The example code will be especially helpful for further understanding the concepts. A good deal of effort has been made to construct adequate examples and implement them. The code examples are constructed to be easy to read and understand. They are written in MATLAB for better legibility.
There is no better programming language than MATLAB at being able to handle the matrices of Deep Learning in a simple and intuitive manner. The example code uses only basic functions and grammar, so that even those who are not familiar with MATLAB can easily understand the concepts.
For those who are familiar with programming, the example code may be easier to understand than the text of this book.
The other kind of reader is one who wants more in-depth information about Deep Learning than what can be obtained from magazines or newspapers, yet doesn’t want to study formally.
These readers can skip the example code and briefly go over the explanations of the concepts. Such readers may especially want to skip the learning rules of the neural network. In practice, even developers seldom need to implement the learning rules, as various Deep Learning libraries are available.
Therefore, those who never need to develop it do not need to bother with it. However, pay closer attention to Chapters 1 and 2 and Chapters 5 and 6. Chapter 6 will be particularly helpful in capturing the most important techniques of Deep Learning, even if you’re just reading over the concepts and the results of the examples.
Equations occasionally appear to provide a theoretical background. However, they are merely fundamental operations. Simply reading and learning to the point you can tolerate will ultimately lead you to an overall understanding of the concepts
What You'll Learn
Use MATLAB for deep learning
Discover neural networks and multi-layer neural networks
Work with convolution and pooling layers
Build a MNIST example with these layers
Who This Book Is For
Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
About the Author
Phil Kim, PhD is an experienced MATLAB programmer and user. He also works with algorithms of large data sets drawn from AI, machine learning. He has worked at Korea Aerospace Research Institute as a Senior Researcher.
There, his main task was to develop autonomous flight algorithm and onboard software for unmanned aerial vehicle. An on-screen keyboard program named 'Clickey' was developed by him during his period in PhD program and served as a bridge to bring the author currently to his current assignment as a Senior Research Officer at National Rehabilitation Research Institute of Korea.
Contents:
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Machine Learning
Chapter 2: Neural Network
Chapter 3: Training of Multi-Layer Neural Network
Chapter 4: Neural Network and Classification
Chapter 5: Deep Learning
Chapter 6: Convolutional Neural Network
About The Book:
Publisher : Apress; 1st ed. edition (June 15, 2017)
Language : English
Pages : 168
File: PDF, 9 MB
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