MIT 6.7960 L03: Optimization Overview — SGD, Adam, LR Schedules & Scaling Rules
From SGD to Adam: pick the right optimizer and scale LR with batch size using scaling rules
4 posts
MIT 6.7960 Fall 2024 OCW Guide
Posts in the MIT 6.7960 Fall 2024 OCW Guide series
MIT 6.7960 L04: Regularization in Practice — Weight Decay, Dropout, Batch Norm & Label Smoothing
Regularization isn't just anti-overfitting — mechanisms & combo strategies for WD, Dropout, BN, Label Smoothing
MIT 6.7960 PS1 Walkthrough: From NumPy MLP to PyTorch Autograd Backprop
Hand-write NumPy MLP + backprop → verify with PyTorch Autograd, fully reproducing OCW HW1 core concepts
MIT 6.7960 L05: CNN Architectures — From Convolution Kernels to Translation Equivariance
Lec 4 core: why CNN is the natural choice for grid data — convolution, translation equivariance, pooling, and classic architectures in one go