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cs229.stanford.edu

CS 229: Machine Learning

The first discussion section will be held on Friday 9/26, in the NVIDIA auditorium, from 4:15 - 5:05 pm. It will cover some materials in linear algebra useful for this course. The first class will be held at 9:00 am on Monday 9/22, in the NVIDIA auditorium. We look forward to seeing you there! Data for problem set 1 can be downloaded here q1x.dat. The project guideline has been released. Please check here. The suggested projects list has been released. Please check here. Materials from the Matlab tutorial.

cs229a.stanford.edu

CS229A - Applied Machine Learning

All future course announcements will be made at http:/ stanford.ml-class.org/. Final Project Guideline: projectGuidelines.pdf. The first class will meet on Monday September 26th, 4.15-5.30pm in Hewlett 103. We hope to see you there! What is machine learning? This class' emphasis is on Applied. How will this class work? This is an online. How can I find out more about the class? Additional information is on the Course Information. Page Common questions are also answered on the FAQ. How do I sign up?

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CS231n Convolutional Neural Networks for Visual Recognition

CS231n Convolutional Neural Networks for Visual Recognition. These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. Feel free to ping @karpathy. If you spot any mistakes or issues, or submit a pull request to our git repo. We encourage the use of the hypothes.is. Extension to annote comments and discuss these notes inline. Assignment #1: Image Classification, kNN, SVM, Softmax. Assignment #2: Neural Networks, ConvNets I. Python / Numpy Tutorial. Gradient...

cs231n.stanford.edu

Stanford University CS231n: Convolutional Neural Networks for Visual Recognition

CS231n: Convolutional Neural Networks for Visual Recognition. This network is running live in your browser. Images into one of 10 classes and was trained with ConvNetJS. Its exact architecture is [conv-relu-conv-relu-pool]x3-fc-softmax, for a total of 17 layers and 7000 parameters. It uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these numbers mean. Class Time and Location. Winter quater (January - March, 2015). Lecture: Monday, Wednesday 2:15-3:30.

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