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Deep Learning For Dummies
book

Deep Learning For Dummies

by John Paul Mueller, Luca Massaron
May 2019
Intermediate to advanced content levelIntermediate to advanced
368 pages
9h 55m
English
For Dummies
Content preview from Deep Learning For Dummies

Chapter 5

Reviewing Matrix Math and Optimization

IN THIS CHAPTER

Bullet Defining the math requirements for simple deep learning

Bullet Performing scalar, vector, and matrix math tasks

Bullet Equating learning with optimization

Chapter 1 of this book tells you about the basis of deep learning and why it’s important today. In Chapter 2, you delve a little deeper into the process of learning something from data through machine learning. A key point from both those chapters is that your computer doesn’t understand anything, but you can provide it with data and, in turn, it can help you understand something new from that data. For example, you can describe a math operation to it that helps you gain insight or understand your data in a way that you couldn’t otherwise. The computer becomes a tool for performing truly advanced math far faster than you could ever do it manually. The basis of these math operations is the use of specific data structures, including the matrix.

You need to understand scalar, vector, and matrix operations as part of discovering how deep learning can make a significant difference in how you view the data that describes the world today. Combining data found in specific kinds of structures ...

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Publisher Resources

ISBN: 9781119543046Purchase book