CNNs and ViT: Turning an Image into Model Inputs | CMU 10-423 Lecture 5
Text models convert text into tokens; image models also need a computational representation of pixels.
#cnn
14 postsBatchNorm, Dropout, CNNs, PyTorch, and RNN Captioning | Stanford CS231N Assignment 2
Assignment 2 of CS231N Spring 2026 is worth 18% of the grade. Across five notebooks you hand-write BatchNorm/LayerNorm, dropout, and the forward and backward passes for convolution and pooling, then learn PyTorch at three levels of abstraction, and finish with RNN image captioning on COCO in PyTorch. Q4 is the turning point: through Q3 you derive every gradient yourself, and from Q5 on autograd takes over while a numerical gradient check confirms it. The official slides warn that this is the longest of the three assignments.
Image Classification with CNNs — From Hand-Crafted Features to Convolution and Pooling | Stanford CS231N Lecture 5
A two-layer network flattens a 32×32×3 image into a 3072-dimensional vector, and the spatial structure is gone. CS231N Lecture 5 answers with two layers. A convolution layer slides small filters across the image and reuses the same weights at every position. A pooling layer downsamples and has no learnable parameters. Both are translation equivariant. One formula gives every layer's output size: (W − K + 2P) / S + 1.
Object Detection, Image Segmentation, and Visualization | Stanford CS231N Lecture 9
Lecture 9 of CS231N Spring 2026 moves from one label per image to one label per pixel and per object. Semantic segmentation uses fully convolutional networks that downsample and then upsample, and U-Net feeds high-resolution features back in. Detection goes from R-CNN's roughly 2,000 CNN forward passes to Fast R-CNN, Faster R-CNN's RPN, single-stage YOLO, and anchor-free DETR. Mask R-CNN adds a 28×28 mask per RoI. The last part covers saliency, CAM, and Grad-CAM. The adversarial examples, DeepDream, and style transfer listed on the schedule appear in neither the 2026 nor the 2025 slides.
Training CNNs and CNN Architectures — Normalization, Initialization, Transfer Learning, and VGG to ResNet | Stanford CS231N Lecture 6
The 2026 slides for CS231N Lecture 6 are titled "Training CNNs and CNN Architectures" and split into how to build and how to train. Only two architectures get case studies. VGG shows that three 3×3 convs are deeper and cheaper than one 7×7. ResNet lets layers learn the residual F(x) = H(x) − x, which fixes an optimization problem where deeper plain nets had worse training error. The most practical takeaway is transfer learning: with fewer than about a million images, start from a model pretrained on a large dataset.
Video Understanding — What Changes When You Add a Time Axis | Stanford CS231N Lecture 10
CS231N Lecture 10 treats video as 2D plus time, a T×3×H×W tensor, and follows one thread: efficiency. Train on short clips and average several clips at test time. Architectures run from per-frame 2D CNNs and late fusion to 3D CNNs, then two-stream networks that isolate motion with optical flow, and I3D, which inflates 2D weights into 3D. After 2021 the field moved to Transformers, where token counts explode, which led to divided space-time attention, Video Swin, MViT, and tubelets. The last part covers temporal localization, audio-visual models, VideoLLMs, and long-form video, where HourVideo shows how far the field still has to go.
How Do You Measure a Model's Size? Parameters, Activations, MACs, and Latency | MIT 6.5940 Lectures 1–2 / Lab 0
The first two lectures of 6.5940 show that the problem exists, then hand you the rulers. L1 plots model parameter counts growing much faster than GPU memory, and contrasts 80GB on a cloud GPU with 320kB on a microcontroller. L2 splits efficiency metrics into memory metrics (#parameters, model size, peak activations) and compute metrics (MAC, FLOP, OP). AlexNet has 61M parameters and 724M MACs, and on a microcontroller the thing that runs out first is usually activation memory, not parameters. Lab 0 introduces a VGG variant on CIFAR-10 (9.2M parameters, 606M MACs) that later labs build on.
Pruning I: Where to Prune, How Fine, and by What Criterion | MIT 6.5940 Lecture 3
Pruning removes unimportant weights or neurons from a neural network. The goal is written as minimizing loss subject to at most N nonzero weights. Lecture 3 of 6.5940 handles two of the decisions involved. First, granularity: from fine-grained pruning, which can remove any element, to channel pruning, which removes whole channels. The more regular the pattern, the easier it is to speed up on existing hardware, and the less you can remove. In between, 2:4 sparsity gives up to 2× speedup on NVIDIA Ampere GPUs. Second, criteria: look at weight magnitude, Batch Norm scaling factors, second derivatives, the fraction of zero activations, or how well a layer's output can be reconstructed after pruning.
ResNet/Transformer Paper Questions and Implementing CNN, ResNet, Transformer, DNABERT, and ConvNeXt | Berkeley CS189 Assignment 4
HW4 has three pieces. The written part has you read ResNet and Attention Is All You Need in the order problem → existing work → proposal → method → contribution. The 4.1 notebook builds a CNN and ResNet-18 in PyTorch, then assembles an encoder-decoder transformer step by step from softmax, trains it on TinyStories, and generates stories. The 4.2 notebook cuts DNA into 6-mers for a pretrained DNABERT to classify species, and turns audio into spectrograms for ConvNeXt, comparing training from scratch, a frozen backbone, and full unfreezing. Two Kaggle competitions; due 5/1. Outside readers get the problems but not the course data bundle or tests.
Initialization, BatchNorm, CNNs, Early Stopping, and Double Descent | Berkeley CS189 Lectures 19–20
Lec 19 wraps up backprop, then tackles how to keep gradients flowing: all-zero initialization makes every unit learn the same thing, so use small random values (He init for ReLU), and batch norm normalizes pre-activations with mini-batch means and variances. The second half introduces CNNs: local connectivity plus weight sharing lets one feature detector scan the whole image. Lec 20 finishes pooling, receptive fields, and CNN training, then covers early stopping, dropout, and double descent, which breaks the classic bias-variance picture.
Neural Networks — Backpropagation, TensorFlow/Keras, CNN & Traffic Sign Classification | Harvard CS50 AI Week 5
Week 5 is about neural networks: a unit multiplies its inputs by weights, adds a bias and applies an activation function; gradient descent adjusts the weights one small step at a time, and backpropagation passes the error back so hidden layers can be trained; for images, convolution and pooling come first and an ordinary network follows, which is a CNN; for sequences, an RNN feeds its output back into itself. With time links for every section, plus the approach, common mistakes and checks for the Traffic project.
CNN Architectures — From Convolution Kernels to Translation Equivariance | MIT 6.7960 Lecture 4
Lec 4 core: why CNN is the natural choice for grid data — convolution, translation equivariance, pooling, and classic architectures in one go
Modern CNN Architectures — ResNet, EfficientNet, ConvNeXt | MIT 6.7960 Topic Guide
ResNet's skip connections solve degradation, enabling 100+ layer nets; EfficientNet compound scales depth/width/resolution; ConvNeXt absorbs Transformer design to reclaim CV crown.
Encoding Image Structure with Convolutional Networks | CMU 07-280 Lecture 14
Lecture 14 replaces dense image models with local connectivity and parameter sharing, moving from convolution, stride, padding, and pooling to AlexNet, GPU data parallelism, ResNet skip connections, and BatchNorm.