The Math of Neural Networks

There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network?

On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns.

In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network.

• Machine Learning with Neural Networks: An In-depth Visual Introduction with Python: Make Your Own Neural Network in Python: A Simple Guide on Machine Learning with Neural Networks.
• Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees
• Bayes' Theorem Examples: A Visual Introduction For Beginners
• Make Your Own Neural Network
• Hypothesis Testing: A Visual Introduction To Statistical Significance
• Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners
• Mastering Deep Learning Fundamentals with Python: The Absolute Ultimate Guide for Beginners To Expert and Step By Step Guide to Understand Python Programming Concepts
• Linear Regression And Correlation: A Beginner's Guide
• Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) (Machine Learning From Scratch Book 1)
• Introduction to the Math of Neural Networks