Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 4. Training Neural Networks

When you hear the term neural networks, it gives you a sense that its a form of biological terminology pertaining to brains. And I have to tell you candidly that it's a no brainer to guess that and, in fact, we are treading along the right path by doing so. We will see how it is connected to that.

Neural networks have brought in a revolution in the data science world. Until 2011, due to not having enough computation power, the people rooting for neural networks were not able to propagate it to the extent that they wanted. But, with the advent of cheaper computation solutions and more research in the area of neural networks, they have taken the data science and artificial world by storm. Neural networks are an algorithm that can be applied in both supervised and unsupervised learning. With deeper networks, they are able to provide solutions to unstructured data, such as images and text.

In this chapter, we will cover the following topics:

  • Neural networks
  • Network...