Understanding Neural Networks (Somewhat)
summary
Claims that feed‑forward nets can approximate any continuous function (universal approximation theorem) but may need exponentially many neurons; shows a 2D ring classification that needs three hidden nodes to perfectly isolate the inner region and sine‑regression where ~10 hidden neurons reproduce the curve. Explains training by backprop and gradient descent sensitivity (wrong learning rate bounces or lands in local minima), demonstrates adversarial fragility (few‑pixel changes flip labels), and recommends GPU acceleration and tools like PyTorch and TensorFlow for experiments.
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chapters (8)
- 0:00Start
- 6:03How to guess the rules?
- 7:21What is Machine Learning?
- 17:20What is a neural network
- 21:07Approximation Theorem
- 29:40Activation Functions
- 54:26Loss functions: Custom design
- 55:20Simple Problem
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