MATLAB Deep Learning

With Machine Learning, Neural Networks and Artificial Intelligence

verfasst von: Phil Kim

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.
What You'll LearnUse 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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Machine Learning

Abstract

However, experts generally distinguish them. If you have decided to study this field, it’s important you understand what these words actually mean, and more importantly, how they differ.

Phil Kim

Chapter 2. Neural Network

Abstract

This chapter introduces the neural network, which is widely used as the model for Machine Learning. The neural network has a long history of development and a vast amount of achievement from research works. There are many books available that purely focus on the neural network. Along with the recent growth in interest for Deep Learning, the importance of the neural network has increased significantly as well. We will briefly review the relevant and practical techniques to better understand Deep Learning. For those who are new to the concept of the neural network, we start with the fundamentals.

Phil Kim

Chapter 3. Training of Multi-Layer Neural Network

Abstract

In an effort to overcome the practical limitations of the single-layer, the neural network evolved into a multi-layer architecture. However, it has taken approximately 30 years to just add on the hidden layer to the single-layer neural network. It’s not easy to understand why this took so long, but the problem involved the learning rule. As the training process is the only method for the neural network to store information, untrainable neural networks are useless. A proper learning rule for the multi-layer neural network took quite some time to develop.

Phil Kim

Chapter 4. Neural Network and Classification

Abstract

Classification is used to determine the group the data belongs. Some typical applications of classification are spam mail filtering and character recognition. In contrast, regression infers values from the data. It can be exemplified with the prediction of income for a given age and education level.

Phil Kim

Chapter 5. Deep Learning

Abstract

You don’t need to be nervous though. As Deep Learning is still an extension of the neural network, most of what you previously read is applicable. Therefore, you don’t have many additional concepts to learn.

Phil Kim

Chapter 6. Convolutional Neural Network

Abstract

The importance of the deep neural network lies in the fact that it opened the door to the complicated non-linear model and systematic approach for the hierarchical processing of knowledge.

Phil Kim

Backmatter

Titel: MATLAB Deep Learning
verfasst von: Phil Kim
Verlag: Apress
Electronic ISBN: 978-1-4842-2845-6
Print ISBN: 978-1-4842-2844-9
DOI: https://doi.org/10.1007/978-1-4842-2845-6