Deep Learning for Computer Vision (Andrej Karpathy, OpenAI)
Andrej Karpathy, Hugo Larochelle, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta, lexfridman
This presentation traces the evolution of convolutional neural networks from 1960s neuroscience foundations to the 2012 AlexNet breakthrough, highlighting how deep learning displaced traditional feature extraction by achieving near-human accuracy on the ImageNet dataset. Key architectural innovations, such as residual skip connections in ResNets and efficient Inception modules, enabled the training of deeper, wider models that serve as generic feature extractors for diverse tasks ranging from object detection to medical imaging. The discussion concludes with practical deployment strategies emphasizing the use of pre-trained models and GPU-accelerated infrastructure to overcome computational bottlenecks in both cloud and edge environments.