Study backpropagation and gradient descent by writing a simple neural network from scratch in Python - without any libraries, just the basics. Perfect for future machine learning engineers, data specialists, and AI developers who want to gain a deeper understanding of neural networks.
Course Overview
This course unveils the essence of neural networks through 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.
Learning Outcomes
- Program Neural Networks: How to program neural networks from scratch using only Python.
- Understand Backpropagation: What backpropagation is and how it helps train models effectively.
- Simplify Complex Mathematics: How to break down complex mathematics into simple, executable steps.
- Grasp Gradients: The simplest way to understand what gradients are and why they are important.
- Model Predictions: What really happens when a machine makes predictions.
- Train Smarter Models: How to train a smarter model by adjusting the smallest details in the code.
Course Structure
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 are required!
Why Take This Course?
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. It offers a hands-on approach that empowers you to explore the fundamental principles behind neural network operations.
