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Deep networks are notoriously unstable. Batch Normalization keeps them in check.
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The most counter-intuitive trick in Deep Learning: Randomly deleting parts of your brain!
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You shouldn't use the exact same step size (Learning Rate) for the entire hike down the mountain.
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Content for Loss Functions goes here.
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Backpropagation calculates the "blame" (gradients). But the Optimizer is the worker who actually grabs a wrench and updates the weights!
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How do we pick the starting weights before the model even begins training?