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

saved by

• Dat Nguyen
• Clementine Yang

related reading

• lecture_2.pdfcs231n.stanford.edu
• Lecture 2.pptxcs231n.stanford.edu
• Support vector machine - Wikipediaen.wikipedia.org
• CS231n Deep Learning for Computer Visioncs231n.github.io
• Neural networks and deep learningneuralnetworksanddeeplearning.com
• Support vector machine - Wikipediaen.wikipedia.org
• CS231n Deep Learning for Computer Visioncs231n.github.io
• CS231n Deep Learning for Computer Visioncs231n.github.io
• Deep Learning using Linear Support Vector Machinesarxiv.org
• Feature Visualizationdistill.pub
• Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
• ImageNet Classification with Deep Convolutional Neural Networksproceedings.neurips.cc