Deep Neural Nets: 33 years ago and 33 years from now

Deep Neural Nets: 33 years ago and 33 years from now

The Yann LeCun et al. (1989) paper Backpropagation Applied to Handwritten Zip Code Recognition is I believe of some historical significance because it is, to my knowledge, the earliest real-world application of a neural net trained end-to-end with backpropagation. Except for the tiny dataset (7291 16x16 grayscale images of digits) and the tiny neural network used (only 1,000 neurons), this paper reads remarkably modern today, 33 years later - it lays out a dataset, describes the neural net architecture, loss function, optimization, and reports the experimental classification error rates over training and test sets....

Published in karpathy.github.io · by Andrej Karpathy · 13 min read · August 30, 2023
What I learned from competing against a ConvNet on ImageNet

What I learned from competing against a ConvNet on ImageNet

The results of the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) were published a few days ago. The New York Times wrote about it too. ILSVRC is one of the largest challenges in Computer Vision and every year teams compete to claim the state-of-the-art performance on the dataset. The challenge is based on a subset of the ImageNet dataset that was first collected by Deng et al. 2009 , and has been organized by our lab here at Stanford since 2010....

Published in karpathy.github.io · by Andrej Karpathy · 15 min read · August 30, 2023