awesome-courses/data.json
2015-03-30 21:27:51 +03:00

488 lines
19 KiB
JSON

[
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"title": "Foundations of Cryptography",
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"title": "Complexity Theory",
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{
"id": 32,
"title": "Information Retrieval",
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"link": "http://www.cs.cornell.edu/courses/CS4300/2013fa/",
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"category": "machine learning",
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"title": "Convex Optimization",
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"university": "Stanford University",
"category": "machine learning",
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"link": "http://www.cs.cmu.edu/~tom/10701_sp11/",
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"title": "Introduction to Matrix Methods",
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},
{
"id": 38,
"title": "Convolutional Neural Networks for Visual Recognition",
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"link": "http://cs231n.stanford.edu/",
"description": "Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.",
"university": "Stanford University",
"category": "machine learning",
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},
{
"id": 39,
"title": "Statistical and Discrete Methods for Scientific Computing",
"code": "CS395T",
"link": "http://www.nr.com/CS395T/",
"description": "Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets. The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++.",
"university": "University of Texas",
"category": "machine learning",
"assignments": "http://granite.ices.utexas.edu/coursewiki/index.php/Main_Page",
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},
{
"id": 40,
"title": "Machine Learning",
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"link": "http://www.cs.columbia.edu/~jebara/4771/",
"description": "Machine learning course taught by Tony Jebara",
"university": "Columbia University",
"category": "machine learning",
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