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This lecture explains parameterized models, loss functions, gradient-based optimization, and backpropagation in neural networks. It includes a PyTorch implementation, generalized backpropagation, Jacobian examples, gradient computation for basic modules, and practical techniques.
Syllabus
– Week 2 – Lecture
– Gradient Descent Optimization Algorithm
– Advantages of SGD, Backpropagation for Traditional Neural Net
– PyTorch implementation of Neural Network and a Generalized Backprop Algorithm
– Basic Modules - LogSoftMax
– Practical Tricks for Backpropagation
– Computing gradients for NN modules and Practical tricks for Back Propagation
Taught by
Alfredo Canziani