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Stanford CS231N — CNNs and Computer Vision Study Pack
Use this as a lecture map before watching, a review guide between classes, or a timestamped index when you need to revisit one concept. CS231N is taught by Fei-Fei Li and Andrej Karpathy at Stanford.
Interactive Stanford CS231N study pack
CS231N covers image classification, convolutional neural networks, object detection, segmentation, and visual recognition architectures.
Image classification with CNNs introduces the architecture stack: convolution, batch norm, activation, and pooling layers.
Training CNNs covers optimization, learning rate schedules, data augmentation, and transfer learning strategies.
R-CNN, Fast R-CNN, and Faster R-CNN address object detection by combining region proposals with CNN feature extraction.
Fully convolutional networks and U-Net handle semantic segmentation by predicting a class for every pixel.
Visualizing what CNNs learn covers activation maximization, deconvolution, and gradient-based saliency methods.
What this lesson teaches
CS231N is Stanford's comprehensive computer vision course, focusing on deep learning for image recognition. The course moves from image classification fundamentals through CNN architectures, then covers object detection, semantic segmentation, and modern vision transformers. Taught by Fei-Fei Li and Andrej Karpathy, the course emphasizes both mathematical foundations and practical implementation.
Key concepts and takeaways
- Image classification assigns a semantic label to an image; CNNs exploit spatial structure through weight sharing across the input.
- A CNN stack alternates convolution layers (feature extraction) with pooling layers (spatial reduction) to build hierarchical representations.
- Transfer learning — starting from a pretrained model and fine-tuning on a smaller dataset — is the standard approach for most practical vision tasks.
- Object detection predicts both class labels and bounding boxes, combining classification with localization.
- Semantic segmentation assigns a class label to every pixel, enabling pixel-precise object localization.
- CNN visualization techniques reveal what features each layer responds to, helping debug and interpret model behavior.
Review checklist
Implement a simple two-layer CNN for CIFAR-10 and observe how accuracy changes with different filter sizes and learning rates.
Fine-tune a pretrained ResNet on a small custom image dataset and compare results with and without data augmentation.
Apply a pretrained object detection model to a dataset of interest and evaluate detection precision on representative images.
Run a semantic segmentation model on an image and visualize the per-pixel class predictions overlaid on the original.
Lesson chapters
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