Neural Network in C# from Scratch

Neural Network and Backpropagation coding deep dive with C#

What you'll learn
Implement Neural Network from scratch using C# code
Understand Neural Network structure and functions by coding
Get familiar with theoretical concepts surrounding Neural Networks
Use DDD to model Neural Network
Use iterative and functional development style
Understand how Neural Network theory transforms into practice with C# code

Requirements
Basic .NET knowledge is helpful, but above all interest in development and machine learning

Description
I am sure you heard about neural networks, machine learning and transformers. Maybe you are already familiar with some of the concepts surrounding these fields, or even tried a practical approach already, but still feel you are missing something.I know I have felt this way even after taking several courses and learning special libraries(python I am looking at you). I always felt I somehow missed the point. That is why I created this hands on course, where together we go over main features of Neural Networks including:LayersNeuronsConnectionsFeed ForwardBackpropagationVisualizing the LossWe will use our own deep neural network diagram, created specifically for this course. Using such graphical approach will make it easier to understand what we are coding, model by model.Specific emphasis is put on backpropagation, where I guide you through an article with step by step explanations of partial derivatives calculation for our diagram.Once we build our neural network we also test it on more demanding functions and see how we can improve predictions.We use object oriented modelling and a bit of functional programming along the way.So, if you are interested in a practical coding approach to understanding neural networks, join me in this course.

Who this course is for
.NET developers interested in machine learning and neural networks