Machine Learning Fundamentals · free preview
What This Course Leaves Undone
The pipeline built across these lessons scores fixed-length inputs with a differentiable objective, and the architectures that handle sequences, images and generative tasks extend the same loop rather than replacing it.
1. The scaffold, restated as components
The scaffold, restated as components
2. Extensions, each touching one component
Extensions, each touching one component
3. Where the shape assumptions break first
Where the shape assumptions break first
4. What to check when a run goes wrong
What to check when a run goes wrong
5. Which component a new architecture changes
A practitioner replaces a fully connected layer with a convolutional layer in an image classifier. Which parts of the training procedure change?
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