Introduction

NeuralNet is a neural network library that is great both for a beginner looking to get started quickly, and a technical expert wishing to implement their own features. It is built on the Vector<double> and Matrix<double> types in Math.NET Numerics.

Creating your first Neural Network

To begin, read the article on creating your first neural network. This will teach you how to make a very basic neural network, introducing you to all the key tools and concepts.

Next Steps

Once you are comfortable with the basics, start exploring the features in NeuralNetFactory and NeuralNet. This will introduce you to advanced concepts that have been overlooked in the previous article, such as GradientDescender and CostFunction.

Outside Research

Machine learning is a heavily researched and documented field, and there are lots of resources to help you learn more. Below are a list of links that taught me the technical details behind machine learning:

• Very brief overview of what a neural network is: Why do Neural Networks Need an Activation Function? (up to "What does a Neuron do?")
• The mathematics behind machine learning: 3blue1brown, machine learning playlist
• An overview of cost functions and gradient descent: Machine learning fundamentals (I): Cost functions and gradient descent
• Activation functions explained: Understanding Activation Functions in Neural Networks
• Various Gradient Descent methods compared: An overview of gradient descent optimisation algorithms