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

Deep neural networks


Let's recap on what we learned in Chapter 4, Training Neural Networks. A neural network is a machine emulation of the human brain that is seen as asetof algorithms that have been set out to extract patterns out of data. It has got three different layers:

  • Input layer
  • Hidden layer
  • Output layer

Sensory numerical data (in the form of a vector) passes through the input layer and then goes through the hidden layers to generate its own set of perceptions and reasoning to yield the final result in the output layer.

 

Can you recall what we learned in Chapter 4, Training Neural Networksregarding the number of layers in ANN and how we count them? When we have got the layers like the ones shown in the following diagram, can you count the number of layers? Remember, we always count just the hidden layer and the output layer. So, if somebody is asking you how many layers there are in your network, you don't include the input layer while answering:

Yes, that's right—there are two layers...