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

Neural networks


Let me explain first of all what neurons are and how they are structured. The following labelled diagram shows a typical neuron:

We define neuron as an electrically excitable cell that receives, processes, and transmits information through electric and chemical signals. A dendrite is a part of it that receives signals from other neurons. One thing that we need to pay attention to is that just a single neuron can't do anything and there are billions of neurons connected to each other, which enables the electro-chemical signal flow and, in turn, the information to flow through it. The information passes through an axon and a synapse, before being transmitted.

When it comes to a neural network, the structure doesn't change much. Let's have a look at it. In the middle, we have a neuron and this neuron gets signals from three other neurons, X1, X2, and X3. All three neurons are connected by arrows that act like a synapse. These neurons, X1, X2, and X3, are called input layer neurons...