As someone in AI, which concept blew your mind away when you first learnt about it?
For me? It was GradCAM was a gamechanger at selling computer vision initiatives internally to the non-technical stakeholders. The gradCAM function computes the importance map by taking the derivative of the reduction layer output for a given class with respect to a convolutional feature map. If you have a 5 layered convolutional neural network, then you can use this against any layer and check the class activation map. The best explanation to give is: "Regions in Red are the areas of the image that the neural network is looking at to make a decision" Well to be honest, regions in red represent the class activations arising from that region but, it would be too much for normie business guys.
tyrell
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by Gooner7
Goldman Sachs
ImageNet Classification with Deep Convolutional Neural Networks
The 2012 breakthrough by Krizhevsky(of AlexNet fame), Sutskever(Co-Founder at OpenAI), and Hinton(Godfather of AI) with AlexNet revolutionized AI. By using deep convolutional neural networks and leveraging GPUs for training, they achieved insane accuracy on ImageNet dataset. This not only validated deep learning's potential but also introduced key innovations like ReLU activations, dropout, and data augmentation.
Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf