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.

• Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
• Bayes' Theorem Examples: A Visual Introduction For Beginners
• Decision Trees and Random Forests: A Visual Introduction For Beginners
• Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
• Make Your Own Neural Network
• Hypothesis Testing: A Visual Introduction To Statistical Significance
• Algorithms Illuminated: Part 1: The Basics
• Neural Networks and Deep Learning: Deep Learning explained to your granny - A visual introduction for beginners who want to make their own Deep Learning Neural Network (Machine Learning)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Machine Learning: An Applied Mathematics Introduction