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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Basic Terminology

Section 2: Creating our Models

Lecture 3 Modelling Neural Network

Lecture 4 Modelling Layer

Lecture 5 Modelling Neuron

Lecture 6 Modelling Activations 1

Lecture 7 Modelling Activations 2

Lecture 8 Modelling Connections

Lecture 9 Modelling Recap

Section 3: Training our Neural Network

Lecture 10 Section Overview

Lecture 11 Modelling Train data

Lecture 12 Modelling Feed Forward 1

Lecture 13 Modelling Feed Forward 2

Lecture 14 Backpropagation Intro

Lecture 15 Backpropagation Derivatives

Lecture 16 Modelling Backpropagation

Lecture 17 Modelling Weight Updates

Lecture 18 Modelling Predict Function

Lecture 19 Testing Our Neural Network

Lecture 20 Visualizing the Loss

Lecture 21 Advanced Function

Section 4: Wrap Up

Lecture 22 Congratulations

.NET developers interested in machine learning and neural networks