The course that trained a generation of computer vision engineers — still the best academic deep dive into CNNs and vision available.
About this course
CS231n is one of the most influential academic courses ever taught in machine learning — the course where convolutional neural networks for image recognition became mainstream, first delivered by Fei-Fei Li and Andrej Karpathy at Stanford. It covers convolutional network architecture in depth, backpropagation, training techniques (batch normalization, dropout, learning rate scheduling), object detection frameworks (YOLO, Faster R-CNN), image segmentation, recurrent networks for sequence tasks, and generative models (GANs, VAEs).
The course materials — lecture videos, slides, and assignments — have been publicly available for years and have trained more practicing ML engineers than possibly any other single resource outside of a formal degree. Karpathy's lecture style and the depth of the assignments set a benchmark that few online courses match.
What you'll learn
Implement convolutional neural networks from scratch and understand their mechanics
Apply training techniques: batch normalization, dropout, and learning rate scheduling
Build and use object detection frameworks including YOLO and Faster R-CNN
Implement image segmentation models
Understand and implement generative models (GANs and VAEs)
This course includes
50h
On-demand video
Yes
Mobile access
English
Language
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Instructor
FL
Fei-Fei Li / Andrej Karpathy
Stanford Online instructor
1.5M+ learners5 courses4.9 instructor rating
Originally taught by Fei-Fei Li (Stanford CS Professor, former Google Cloud AI Chief Scientist) and Andrej Karpathy (former Tesla AI Director, OpenAI founding member, now independent).
Requirements
Solid Python and NumPy skills
Basic understanding of neural networks and backpropagation
Linear algebra and calculus comfort
Who this course is for
ML engineers and researchers who want depth in computer vision specifically
Data scientists moving from tabular data into image and vision applications
Anyone who has taken Andrew Ng's ML courses and wants the next level of depth
About this provider
SO
Stanford Online
Stanford University's online learning platform offering free and paid courses from Stanford faculty across AI, ML, medicine, and computer science.
The CNN fundamentals, backpropagation mechanics, and training techniques are foundational and don't change. Specific architectures evolve rapidly — the course provides the foundation to understand new architectures as they emerge.
The lecture videos and slides are freely available on Stanford Online and YouTube; assignments have been publicly shared by previous students. Some versions include paid certificate options.
The assignments are computationally intensive — Google Colab or similar GPU-enabled environments are typically used for free access to sufficient compute.