This episode breaks down the 'ImageNet Classification with Deep Convolutional Neural Networks' research paper, published in 2012, which details the development and training of a deep convolutional neural network for image classification. The authors trained their network on the ImageNet dataset, containing millions of images, and achieved record-breaking results in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). The paper explores various architectural choices, including the use of Rectified Linear Units (ReLUs) for faster training, data augmentation techniques to combat overfitting, and the innovative "dropout" method for regularisation. The network's performance was significantly improved by the use of multiple GPUs, a novel local response normalisation scheme, and overlapping pooling layers. The paper concludes by demonstrating the network's ability to learn visually meaningful features and by highlighting the potential for future advancements in the field of computer vision through larger, deeper, and more powerful convolutional neural networks.

Audio : (Spotify) https://open.spotify.com/episode/6ObxCaFTOEgwgIFzV3jcUE?si=T1oNrJyTSfWL-zGd7En95Q

Paper: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf