Phase 1: Training Neural Networks · ~75 minutes · Python
Activation Functions
Without nonlinearity, your 100-layer network is a fancy matrix multiply. Activations are the gates that let neural networks think in curves.
Hiring signal: Understanding of activation functions internals
What you will learn
- Implement sigmoid, tanh, ReLU, Leaky ReLU, GELU, Swish, and softmax with their derivatives from scratch
- Diagnose the vanishing gradient problem by measuring activation magnitudes through 10+ layers with different activations
- Detect dead neurons in a ReLU network and explain why GELU avoids this failure mode
- Select the correct activation function for a given architecture (transformer, CNN, RNN, output layer)
Introduction
Type: Build Languages: Python Prerequisites: Lesson 03.03 (Backpropagation) Time: ~75 minutes
Learning Objectives
- Implement sigmoid, tanh, ReLU, Leaky ReLU, GELU, Swish, and softmax with their derivatives from scratch
- Diagnose the vanishing gradient problem by measuring activation magnitudes through 10+ layers with different activations
- Detect dead neurons in a ReLU network and explain why GELU avoids this failure mode
- Select the correct activation function for a given architecture (transformer, CNN, RNN, output layer)
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