CS231n Convolutional Neural Networks for Visual Recognition

Course materials and notes for Stanford class CS231n: Convolutional Neural Networks for Visual Recognition.

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns Case Studies (LeNet / AlexNet / ZFNet / GoogLeNet / VGGNet) Computational Considerations Additional References Convolutional Neural Networks (CNNs / ConvNets) Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter: they are made up of neurons that have learnable weights and biases. Each neuron receives s

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related reading

• Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
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• A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
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• ImageNet Classification with Deep Convolutional Neural Networksproceedings.neurips.cc
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