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Build a Simple Neural Network and Learn Backpropagation
Introduction
Introduction (2:59)
Exercise: Meet Your Classmates and Instructor
Course Resources
Neural Networks, Derivatives, Gradients, Chain Rule, and Gradient Descent
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
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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Understanding How w1 and w2 Affect the Loss
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