开源软件名称（OpenSource Name）：

开源软件地址(OpenSource Url)：

开源编程语言(OpenSource Language)：

开源软件介绍(OpenSource Introduction)：

Last Minute Notes of Machine learning and Deep learning By Jason Brownlee

All Article Source : https://machinelearningmastery.com

1. Mini Course of Machine learning

2. Crash Course in Python for Machine Learning Developers

3. Statistics for Machine Learning

4. Linear Algebra for Machine Learning

5. How to Think About Machine Learning

6. How to Get Better Deep Learning Results

7. Python Machine Learning Mini-Course

8. Crash Course in Recurrent Neural Networks for Deep Learning

9. Crash Course in Convolutional Neural Networks for Machine Learning

10. Crash Course On Multi-Layer Perceptron Neural Networks

11. Super Fast Crash Course in R (for developers)

12. How to Get Started With Deep Learning for Computer Vision

13. How to Get Started with Deep Learning for Time Series Forecasting (7-Day Mini-Course)

14. How to Get Started with Deep Learning for Natural Language Processing (7-Day Mini-Course)

15. Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras

16. Applied Deep Learning in Python Mini-Course

17. CNN Long Short-Term Memory Networks

18. Common Pitfalls In Machine Learning Projects

19. Practical Deep Learning for Coders (Review)

Large Scale Machine Learning Courses (OR) Machine learning Courses for Large Dataset

1. Large Scale Learning (EECS6898,Columbia, 2010): http://www.sanjivk.com/EECS6898/lectures.html
2. Large Scale Learning (CMSC 3590, U of Chicago, 2009): http://ttic.uchicago.edu/~gregory/courses/LargeScaleLearning/
3. Models of Computation for Massive Data (CS7960, U of Utah, 2010) : http://www.cs.utah.edu/~jeffp/teaching/cs7960.html
4. Parallel Distributed Processing (85-419, CMU, 2010): http://www.cnbc.cmu.edu/~plaut/IntroPDP/index.html
5. Machine Learning ( COMS4771, Columbia, 2008): http://hunch.net/~coms-4771/lectures.html
6. Machine Learning (CS4780, Cornell, 2009): http://www.cs.cornell.edu/Courses/cs4780/2009fa/
7. Machine Learning (10-701, CMU, 2011): http://www.cs.cmu.edu/~awm/10701/
8. Machine Learning (CS590, Purdue, 2010): http://www.stat.purdue.edu/~vishy/introml/introml.html
9. Advanced Machine Learning (CS253, Caltech, 2010): http://www.cs.caltech.edu/courses/cs253
10. Advanced Machine Learning (COMS6772, Columbia, 2010): http://www.cs.columbia.edu/~jebara/6772/solutions.html
11. Advanced Machine Learning (CS6784, Cornell, 2010): http://www.cs.cornell.edu/Courses/cs6784/2010sp/
12. Advanced Machine Learning (CSC2535, U of Toronto, 2010): http://www.cs.toronto.edu/~hinton/csc2535/lectures.html
13. Statistical Learning Theory (9.520, MIT, 2011): http://www.mit.edu/~9.520/
14. Computational Learning Theory (Comp 150AML, Tufts, 2008): http://www.cs.tufts.edu/~roni/Teaching/CLT/
15. Large-Scale Simultaneous Inference (Stats 329, Stanford, 2010): http://www-stat.stanford.edu/~omkar/329/
16. Inference, Estimation and Information Processing (EE378, Stanford, 2011): http://www.stanford.edu/class/ee378/reading.html
17. Statistical Signal Processing B (EE378B, Stanford, 2011): http://www.stanford.edu/class/ee378B/refs.html
18. Statistical Machine Learning (Domke, RIT, 2011): http://people.rit.edu/jcdicsa/courses/SML/
19. Unsupervised Learning (CSE291, UCSD, 2011): http://cseweb.ucsd.edu/classes/sp11/cse291-d/#syllabus
20. Adaptive Neural Networks (EE373B, Stanford, 2009): http://www.stanford.edu/class/ee373b/
21. Optimization (10725, CMU, 2010): http://select.cs.cmu.edu/class/10725-S10/schedule.html
22. Convex Optimization I (EE364A, Stanford, 2011): http://www.stanford.edu/class/ee364a/
23. Convex Optimization II (EE364B, Stanford, 2011): http://www.stanford.edu/class/ee364b
24. Dealing with Massive Data (COMS6998,Columbia, 2010): http://www.cs.columbia.edu/~coms699812/
25. Algorithms for Massive Data Sets (CS369, Stanford, 2009):http://www.stanford.edu/class/cs369m/
26. Algorithms for Massive Data Sets (CS493, Princeton, 2002): http://www.cs.princeton.edu/courses/archive/spring02/cs493/schedule.html
27. Algorithms for Massive Data Sets (Gørtz, Witt & Bille, DTU, 2011): https://massivedatasets.wordpress.com/
28. Data Mining: Learning from Large Data Sets (Krause, ETH, 2011): http://las.ethz.ch/courses/datamining-s11/
29. From Languages to Information (CS124, Stanford, 2011):http://www.stanford.edu/class/cs124/
30. Data-Intensive Information Processing Applications (Lin, UMD, 2010): http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/syllabus.html
31. Advanced Algorithm Design (CS521, Princeton, 2006): http://www.cs.princeton.edu/courses/archive/fall06/cos521/
32. Approximation algorithms (CS598, UIUC, 2011): http://www.cs.illinois.edu/class/sp11/cs598csc/
33. Data Stream Algorithms (Muthukrishnan, 2009): http://www.cs.mcgill.ca/~denis/notes09.pdf
34. Information Theory (EE376, Stanford, 2011): http://classx.stanford.edu/View/Subject.php?SubjectID=2011_Q1_EE376_Lec
35. Lectures on Statistical Modeling Theory (Rissanen): http://www.mdl-research.org/pub/lectures.pdf
36. Multimedia Databases and Data Mining (15-826, CMU, 2010): http://www.cs.cmu.edu/~christos/courses/826.S10/schedule.html
37. Distributed Systems (CS525, UIUC, 2011): http://www.cs.uiuc.edu/class/sp11/cs525/sched.htm
38. Distributed Systems (6.824, MIT, 2011): http://pdos.csail.mit.edu/6.824/schedule.html
39. Distributed Systems Courses: http://the-paper-trail.org/blog/?page_id=152
40. Parallel Computing Courses: http://www.cs.rit.edu/~ncs/parallel.html#courses
41. Applications of Parallel computers (CS267, U.C. Berkeley, 2011): http://www.cs.berkeley.edu/~demmel/cs267_Spr11/
42. Programming Massively Parallel Processors with CUDA (CS193G, Stanford) : http://itunes.apple.com/itunes-u/programming-massively-parallel/id384233322#ls=1
43. Parallel Algorithms (15-499, CMU, 2009): http://www.cs.cmu.edu/afs/cs/academic/class/15499-s09/www/
44. Advanced Methods in Matrix Computations: Iterative Methods (CS 336, Stanford, 2006): http://www.stanford.edu/class/cme324/
45. Parallel Numerical Algorithms (CS 554, UIUC, 2008): http://www.cse.illinois.edu/courses/cs554/notes/index.html
46. Scientific Computing for Engineers (CS 594, UTK, 2011): http://web.eecs.utk.edu/~dongarra/WEB-PAGES/cs594-2008.htm
47. Algorithms in the "Real World" (15-853, CMU, 2010): http://www.cs.cmu.edu/afs/cs/project/pscico-guyb/realworld/www/
48. Sublinear Algorithms (6.896, MIT, 2010): http://stellar.mit.edu/S/course/6/fa10/6.896/materials.html
49. Large-Scale Simultaneous Inference (Stats 329, Stanford, 2010): http://www-stat.stanford.edu/~omkar/329/
50. Communication-Avoiding Algorithms (CS294, Berkeley, 2011): http://www.cs.berkeley.edu/~odedsc/CS294/
51. Modeling Data with Uncertainty (Seminar, U of Utah, 2010): http://www.cs.utah.edu/~suresh/mediawiki/index.php/Algorithms_Seminar/Fall10#Schedule