Build a Simple Neural Network & Learn Backpropagation
Course description
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.
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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.
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All Course Lessons (39)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 03:00 | |
| 2 | Introduction to Our Simple Neural Network | 06:49 | |
| 3 | Why We Use Computational Graphs | 06:20 | |
| 4 | Conducting the Forward Pass | 06:56 | |
| 5 | Roadmap to Understanding Backpropagation | 02:48 | |
| 6 | Derivatives Theory | 04:28 | |
| 7 | Numerical Example of Derivatives | 13:40 | |
| 8 | Partial Derivatives | 08:02 | |
| 9 | Gradients | 03:53 | |
| 10 | Understanding What Partial Derivatives DРѕ | 10:14 | |
| 11 | Introduction to Backpropagation | 05:01 | |
| 12 | (Optional) Chain Rule | 07:33 | |
| 13 | Gradient Derivation of Mean Squared Error Loss Function | 07:37 | |
| 14 | Visualizing the Loss Function and Understanding Gradients | 11:39 | |
| 15 | Using the Chain Rule to See how w2 Affects the Final Loss | 18:43 | |
| 16 | Backpropagation of w1 | 04:30 | |
| 17 | Introduction to Gradient Descent Visually | 10:08 | |
| 18 | Gradient Descent | 06:08 | |
| 19 | Understanding the Learning Rate (Alpha) | 08:11 | |
| 20 | Moving in the Opposite Direction of the Gradient | 05:31 | |
| 21 | Calculating Gradient Descent by Hand | 08:48 | |
| 22 | Coding our Simple Neural Network Part 1 | 04:24 | |
| 23 | Coding our Simple Neural Network Part 2 | 07:17 | |
| 24 | Coding our Simple Neural Network Part 3 | 06:32 | |
| 25 | Coding our Simple Neural Network Part 4 | 05:01 | |
| 26 | Coding our Simple Neural Network Part 5 | 05:23 | |
| 27 | Introduction to Our Complex Neural Network | 05:30 | |
| 28 | Conducting the Forward Pass | 04:25 | |
| 29 | Getting Started with Backpropagation | 04:52 | |
| 30 | Getting the Derivative of the Sigmoid Activation Function(Optional) | 07:43 | |
| 31 | Implementing Backpropagation with the Chain Rule | 04:55 | |
| 32 | Understanding How w3 Affects the Final Loss | 06:10 | |
| 33 | Calculating Gradients for Z1 | 07:43 | |
| 34 | Understanding How w1 and w2 Affect the Loss | 04:53 | |
| 35 | Implementing Gradient Descent by Hand | 08:29 | |
| 36 | Coding our Advanced Neural Network Part (Implementing Forward Pass + Loss) | 06:51 | |
| 37 | Coding our Advanced Neural Network Part 2 (Implement Backpropagation) | 10:11 | |
| 38 | Coding our Advanced Neural Network Part 3 (Implement Gradient Descent) | 05:35 | |
| 39 | Coding our Advanced Neural Network Part 4 (Training our Neural Network) | 08:16 |
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