Build a Simple Neural Network & Learn Backpropagation

What you will learn:

• How to program neural networks from scratch using only Python
• What backpropagation is and how it helps train models
• How to break down complex mathematics into simple, executable steps
• The simplest way to understand what gradients are and why they are important
• What really happens when a machine makes predictions
• How to train a smarter model by adjusting the smallest details in the code

This course unveils the essence of neural networks: mathematics and pure Python.

You will delve into the inner workings of backpropagation, gradient descent, and the mathematical foundations on which modern neural networks are built. No ready-made frameworks, no "black boxes" - just you, mathematics, and your code.

Step by step, you will manually build neural networks and implement them from scratch. From partial derivatives to updating weights - each concept will be dissected and implemented in code using Python (no libraries like PyTorch required!).

If you truly want to understand how machine learning works - and prove it by creating your own neural network - this course will be your starting point.