Example Curriculum
Introduction
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Neural Networks, Derivatives, Gradients, Chain Rule, and Gradient Descent
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- Introduction to Our Simple Neural Network (6:48)
- Why We Use Computational Graphs (6:19)
- Conducting the Forward Pass (6:55)
- Roadmap to Understanding Backpropagation (2:47)
- Derivatives Theory (4:27)
- Numerical Example of Derivatives (13:39)
- Partial Derivatives (8:01)
- Gradients (3:52)
- Understanding What Partial Derivatives Dо (10:13)
- Introduction to Backpropagation (5:00)
- (Optional) Chain Rule (7:32)
- Gradient Derivation of Mean Squared Error Loss Function (7:36)
- Visualizing the Loss Function and Understanding Gradients (11:38)
- Using the Chain Rule to See how w2 Affects the Final Loss (18:42)
- Backpropagation of w1 (4:29)
- Introduction to Gradient Descent Visually (10:07)
- Gradient Descent (6:07)
- Understanding the Learning Rate (Alpha) (8:10)
- Moving in the Opposite Direction of the Gradient (5:30)
- Calculating Gradient Descent by Hand (8:47)
- Coding our Simple Neural Network Part 1 (4:23)
- Coding our Simple Neural Network Part 2 (7:16)
- Coding our Simple Neural Network Part 3 (6:31)
- Coding our Simple Neural Network Part 4 (5:00)
- Coding our Simple Neural Network Part 5 (5:22)
Implementing Our Advanced Neural Network by Hand + Python
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- Introduction to Our Complex Neural Network (5:29)
- Conducting the Forward Pass (4:24)
- Getting Started with Backpropagation (4:51)
- Getting the Derivative of the Sigmoid Activation Function(Optional) (7:42)
- Implementing Backpropagation with the Chain Rule (4:54)
- Understanding How w3 Affects the Final Loss (6:09)
- Calculating Gradients for Z1 (7:42)
- Understanding How w1 and w2 Affect the Loss (4:52)
- Implementing Gradient Descent by Hand (8:28)
- Coding our Advanced Neural Network Part (Implementing Forward Pass + Loss) (6:50)
- Coding our Advanced Neural Network Part 2 (Implement Backpropagation) (10:10)
- Coding our Advanced Neural Network Part 3 (Implement Gradient Descent) (5:34)
- Coding our Advanced Neural Network Part 4 (Training our Neural Network) (8:15)
Where To Go From Here?
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