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Episode Info: Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling. ## Resources - Stanford cs231n: Convnets (https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) `course:medium` - Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` - The usual DL resources (pick one): ** Deep Learning Book (http://amzn.to/2tXgCiT) (Free HTML version (http://www.deeplearningbook.org/)) `book:hard` comprehensive DL bible; highly mathematical ** Fast.ai (http://course.fast.ai/) `course:medium` practical DL for coders ** Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) `book:medium` shorter online "book" ## Episode - One-time donations w/ BTC / PayPal - Image recognition, classification - computer vision ** ML takeover ** Final main network (MLP, RNN, CNN) - Don't use MLP for images, use CNNs - Filters -> feature maps -> convolutional layers - Window, stride, padding - Max-pooling - Architectures (ILSVRC ImageNet Challenge) ** LeNet-5 ** AlexNet ** GoogLeNet ** Inception ** Resnet ** etc..Read more ยป

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