[Classic] ImageNet Classification with Deep Convolutional Neural Networks (Paper Explained)

#ai #research #alexnet AlexNet was the start of the deep learning revolution. Up until 2012, the best computer vision systems relied on hand-crafted features and highly specialized algorithms to perform object classification. This paper was the first to successfully train a deep convolutional neural network on not one, but two GPUs and managed to outperform the competition on ImageNet by an order of magnitude. OUTLINE: 0:00 - Intro & Overview 2:00 - The necessity of larger models 6:20 - Why CNNs? 11:05 - ImageNet 12:05 - Model Architecture Overview 14:35 - ReLU Nonlinearities 18:45 - Multi-GPU training 21:30 - Classification Results 24:30 - Local Response Normalization 28:05 - Overlapping Pooling 32:25 - Data Augmentation 38:30 - Dropout 40:30 - More Results 43:50 - Conclusion Paper: https://ift.tt/10K1otj Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar (preferred to Patreon): https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n