Training a Neural Network - Implementing Backpropagation and Gradient Descent from Scratch

Dive into a comprehensive video tutorial on implementing backpropagation and gradient descent from scratch using Python. Learn how to train a neural network to perform arithmetic sum operations. Explore key concepts including data representation, derivatives, reshaping, and the creation of a Natural Language Processing (NLP) model. Follow along as the instructor demonstrates the implementation of backpropagation, testing procedures, and the application of gradient descent. Gain hands-on experience in training a Multilayer Perceptron (MLP) and understand the intricacies of neural network training. Access the accompanying code on GitHub for further practice and experimentation.

Introduction
Data Representation
Derivatives
Reshape
Back propagation
Creating an NLP
Implementing backpropagation
Testing backpropagation
Implementing gradient descent
Applying gradient descent
Printing weights
Testing
Gradient Descent
Train
Train MLP