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Optimization, Machine Learning Models, and TensorFlow (Part 2 of 4)
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[00:13] Optimization (I explain calculus!!!)
[04:40] Gradient descent
[06:26] Perceptron (or linear models – we learned what these are in part 1 but I expound a bit more)
[07:04] Neural Networks (as an extension to linear models)
[09:28] Brief Review of TensorFlow
Hope you enjoy Part 2! As always feel free to send any feedback or add any comments below if you have any questions.
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