✳flâneur — a map of the web's best reading
CS231n Convolutional Neural Networks for Visual Recognition
cs231n.github.io · 9,291 words · saved by 3 readers
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
Explore this link on the map →saved by
related reading
- Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
- A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
- A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
- [2201.03545] A ConvNet for the 2020sarxiv.org
- CS231n Deep Learning for Computer Visioncs231n.github.io
- Feature Visualizationdistill.pub
- Computing Receptive Fields of Convolutional Neural Networksdistill.pub
- Dense CNNsarxiv.org
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- CS231n Deep Learning for Computer Visioncs231n.github.io
- Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
- VGGNet - Wikipediaen.wikipedia.org