Phil Kim

MATLAB Deep Learning

With Machine Learning, Neural Networks and Artificial Intelligence

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Apress / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4842-2844-9
• 1st ed.
• Seitenzahl: 172
• Erscheinungstermin: 15. Juni 2017
• Englisch
• Abmessung: 235mm x 155mm x 10mm
• Gewicht: 272g
• ISBN-13: 9781484228449
• ISBN-10: 1484228448
• Artikelnr.: 48034013

Autorenporträt

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.

Inhaltsangabe

1. Machine Learning.- 2. Neural Network.- 3. Training of Multi-Layer Neural Network.- 4. Neural Network and Classification.- 5. Deep Learning.- 6. Convolutional Neural Network.