Welcome to Lecture 13 of the CS217: AI-ML Course at IIT Bombay, delivered by Prof. Pushpak Bhattacharya. In this deep-dive session, we unravel the mysteries of neural network training through comprehensive exploration of backpropagation and gradient dynamics.
🧠 Topics Covered:
Backpropagation Algorithm: From Theory to Practice
Weight Update Rules: Understanding the Mathematics
Cross-entropy & Softmax: Perfect Pairing Explained
Hidden Layer Training: The Backprop Magic
Gradient Challenges: Vanishing & Exploding Gradients
ReLU Revolution: Solving Training Problems
Delta Propagation: How Errors Flow Backward
Practical Implementation Guidelines for Neural Networks
🔍 Deep Dive Highlights:
Mathematical intuition behind δWji = η × δj × oi
Credit assignment problem in neural networks
Station derivatives and their impact on learning
Real-world implications of activation functions
Evolution from classical sigmoid to modern ReLU
Hidden layer weight update mechanics
💡 Implementation Focus:
Guidelines for lab implementation
Tips for avoiding common pitfalls
Practical considerations for activation functions
Neural network architecture decisions
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