Convolutional Neural Networks (CNN)

A convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.


Common Architectures in Convolutional Neural Networks
In this post, I'll discuss commonly used architectures for convolutional networks.
convolutional-neural-networks architectures computer-vision survey
How to Derive Convolution From First Principles
I derive the convolution from first principles and show that it naturally emerges from translational symmetry.
convolutional-neural-networks article


CS231n: Convolutional Neural Networks for Visual Recognition
Deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.
convolutional-neural-networks computer-vision deep-learning cs231n
Understanding Convolutional Neural Networks for NLP
More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results.
convolutional-neural-networks natural-language-processing text-classification tutorial


PyTorch CNN Trainer
A simple package to fine-tune CNNs from torchvision and Pytorch Image models by Ross Wightman.
torchvision convolutional-neural-networks pytorch training
CNN Explainer
CNN Explainer uses TensorFlow.js, an in-browser GPU-accelerated deep learning library to load the pretrained model for visualization.
convolutional-neural-networks tensorflow-js interactive interpretability
ConvNet Playground
An interactive visualization for exploring Convolutional Neural Networks applied to the task of semantic image search.
interactive convolutional-neural-networks playground data-visualization
Antialiased CNNs
Making Convolutional Networks Shift-Invariant Again.
convolutional-neural-networks shift-invariant paper video
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