CS231n Convolutional Neural Networks for Visual Recognition

Course materials and notes for Stanford class CS231n: Deep Learning for Computer Vision.

Table of Contents: Linear Classification Parameterized mapping from images to label scores Interpreting a linear classifier Loss function Multiclass Support Vector Machine loss Practical Considerations Softmax classifier SVM vs. Softmax Interactive web demo Summary Further Reading Linear Classification In the last section we introduced the problem of Image Classification, which is the task of assigning a single label to an image from a fixed set of categories. Moreover, we described the k-Nearest Neighbor (kNN) classifier which labels images by comparing them to (annotated) images from the tra

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related reading

• Support vector machine - Wikipediaen.wikipedia.org
• Support vector machine - Wikipediaen.wikipedia.org
• Neural networks and deep learningneuralnetworksanddeeplearning.com
• CS231n Deep Learning for Computer Visioncs231n.github.io
• CS231n Deep Learning for Computer Visioncs231n.github.io
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• Feature Visualizationdistill.pub
• Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
• Neural Networks, Manifolds, and Topology -- colah's blogcolah.github.io
• A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
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• The Little Book of Deep Learningfleuret.org