Hi everyone,

When I started playing around with neural networks, I had a lot of questions regarding the theory of neural networks, and while many things were taken for granted, I always wanted to dig into more detail when it comes to how neural networks work.

All this made me want to make an easy to follow tutorial regarding the theory behind Deep Learning and Neural Networks, that would be descriptive and clear enough for the beginners to follow, but also a good recap for people already familiar with the theory.

THE ENTIRE YOUTUBE TUTORIAL PLAYLIST CAN BE FOUND HERE

The tutorial is still underway and more videos are added in about a weekly basis.

 

 


01 – Deep Learning Theory Introduction

 


02 – Classification basics & Perceptrons

An easy to understand introduction to Classification and Perceptronstheory. We start by defining in depth the very basics, like weights, and then moving further to the foundation of Deep Learning theory. The Perceptron.


03 – Perceptron Algorithm, Error Functions, Sigmoid & Softmax Activation Functions


04 – Maximum Likelihood, Cross-Entropy, One-Hot Encoding


05 — Regression, MAE & MSE Error Functions

 

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