Courses, Stanford Online

Stanford CS231N Deep Learning for Computer Vision I 2025

18 lectures, 21 hr 13 min in all

XCS231N Deep Learning for Computer Vision, the professional education version of the graduate course CS231N Deep Learning for Computer Vision, begins August 3rd and runs through October 11th. Enrollment is open now. Learn course details: https://stanford.io/XCS231N-YouTube Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This playlist contains lectures from the graduate course, CS231n Deep Learning for Computer Vision, which is a deep dive into deep learning methods with a focus on end-to-end models for core vision tasks, alongside modern approaches such as transformers, diffusion models, and visual-language models that power today’s AI systems. During the 10-week graduate course, students learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment gives them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks. https://online.stanford.edu/courses/cs231n-deep-learning-computer-vision