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Course contents

Course overview

Read a tensor before training a model
Follow a prediction into its loss
Trace responsibility backward
Decide what the model is allowed to learn from
Give each training-loop line a job
Ask what extra flexibility actually buys
When fitting better means predicting worse
Debug the learning process before enlarging the model
A tiny convolutional network you can account for
Turn a successful run into a reproducible experiment
Two bridges beyond the core: reuse and attention
Defend one small neural experiment end to end

Practice and references

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