Lesson 301
Neural Networks & Deep Learning
Neurons · Backprop · Why Depth Wins
1:00How a stack of simple artificial neurons — each computing a weighted sum plus a bias through a nonlinearity — can learn arbitrarily complex functions via backpropagation and gradient descent.
By the end, you can
- Compute the weighted sum z and the sigmoid output a for a given neuron by hand.
- Explain why at least one nonlinear activation is required between layers.
- Trace a complete training step: forward pass, loss, backprop, gradient descent.
- Describe what the learning rate controls and how the gradient-descent update is written.
- Explain what depth buys: a learned feature hierarchy built layer by layer.
- Match each architecture family (CNN, RNN/LSTM, Transformer) to the data it is designed for.
- Distinguish overfitting from underfitting, and explain how dropout mitigates it.
- Correct the misconception that neural networks work like biological brains.
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