Build a Simple Neural Network & Learn Backpropagation

Name: Build a Simple Neural Network & Learn Backpropagation
Price: 9 USD
Availability: InStock

4h 34m 9s

English

Paid

June 19, 2025

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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#	Title	Duration
1	Introduction	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