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1 | Timestamp | Resource URL | Resource Type | What topic(s) does this resource cover? | Why is this a useful resource? What did you find useful or enjoy about it? (Please limit to 1-2 sentences) | Email Address | ||||||

2 | 6/1/2017 20:19:56 | pgad@umich.edu | Machine Learning: A Probabilistic Perspective (available on Amazon/in the Library) | Textbook | General topics in Machine Learning | He explains all the topics he covers really well, and gives great statistical perspective. | ||||||

3 | 6/1/2017 20:52:02 | samtenka@umich.edu | Wasserstein GAN | Paper | Generative Adversarial Deep Learning without Tears | WGANs are the future. This seminal paper both explains the technique lucidly and inspires a more general understanding of neural net training. I'd recommend anyone who is excited about deep unsupervised learning to peruse this paper. | ||||||

4 | 6/1/2017 20:54:14 | samtenka@umich.edu | https://www.tensorflow.org/get_started/mnist/pros | Tutorial/Coding Demo | Tensorflow Basics | If you understand CNNs in theory, this rapid yet clear tutorial will get you started with their practical implementation. Pairs nicely with LeCun's paper introducing CNNs. | ||||||

5 | 6/1/2017 20:58:28 | samtenka@umich.edu | https://work.caltech.edu/textbook.html | Textbook | Statistical Learning Theory; Data Science Basics | Grounds the theoretically oriented beginner in the philosophies and tools of machine learning. I would highly recommend this book to physicists, cows, and those who ask "why?" more than "how?". The book might or might not be available for free online. | ||||||

6 | 6/1/2017 21:01:57 | samtenka@umich.edu | https://www.google.com/url?sa=t&source=web&rct=j&url=https://m.youtube.com/watch%3Fv%3D_PwhiWxHK8o&ved=0ahUKEwjbm9jP-p3UAhXKDcAKHa5hBIUQwqsBCB4wAA&usg=AFQjCNGD7ytIAA4EThEOvqTCShF0faDLhA&sig2=_p0Pm1oaM-JwwGk8Hn7DoA | Lecture Video | SVM | Avuncular and expert, Patrick Winston takes us on a leisurely stroll through the intuition and implementation of SVMs. Let this video (played at 1.5 speed) be your guide to this important class of models. | ||||||

7 | 6/1/2017 21:06:41 | samtenka@umich.edu | https://www.google.com/url?sa=t&source=web&rct=j&url=http://www.nvidia.com/docs/io/116711/sc11-cuda-c-basics.pdf&ved=0ahUKEwiopebN-53UAhWM2YMKHY4jBsQQFggnMAE&usg=AFQjCNED8luBDHCGYvyJ7ZnVKuyElykIyQ&sig2=I4HTAY9u5CaGglQph5eVeQ | Tutorial/Coding Demo | CUDA | Get your hands dirty with low-level GPU computing as quickly as possible by following these slides! I thought they were fun, and so can you. | ||||||

8 | 6/1/2017 21:11:18 | samtenka@umich.edu | https://goo.gl/forms/5LTmkaPsFWdCqlY82 | Webpage | SGD et al. | Nesterov, Adam, RMSProp... what a mess! This unsystematic but insightful comparison helps us master the menagerie of gradient-based optimizers. It's often more clear than then corresponding papers, too. | ||||||

9 | 6/7/2017 18:09:40 | pktan@umich.edu | https://github.com/jakevdp/PythonDataScienceHandbook | Jupyter Notebooks | Python Data Science Packages | Covers most of the python packages that beginners need to get started. It's actually a book, but the author decided to open-source it, so do ask the members to buy the book and support the author if they like it. | ||||||

10 | 6/13/2017 11:05:51 | samtenka@umich.edu | http://karpathy.github.io/2015/05/21/rnn-effectiveness/ | Tutorial/Coding Demo | RNNs | Turned me on to Recurrent Neural Nets | ||||||

11 | 6/13/2017 19:09:27 | jpgard@umich.edu | http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf | Textbook | Supervised and unsupervised learning, data mining, R | This is a great overview of many of the core machine learning techniques, and doubles as a user-friendly introduction to R. The book is well-written and filled with insights. The same authors also have a great series of videos aligned with the book chapters (available on YouTube) and, for more advanced reading or more in-depth coverage, a similar book entitled Elements of Statistical Learning. | ||||||

12 | 6/13/2017 19:14:39 | jpgard@umich.edu | https://www.amazon.com/dp/1449316956/ref=cm_sw_r_cp_dp_T2_Gehqzb9KV6QWX | Textbook | R, GGplot2 | As a hardcore R user, I often find myself looking for a reference to adjust the same things on my visualizations -- how to I remove axis ticks, add text labels to graphical elements, or adjust legends? This is my go-to resource. A "free" PDF is floating around the internet. | ||||||

13 | 6/13/2017 19:21:20 | jpgard@umich.edu | https://www.coursera.org/specializations/data-science-python | MOOC | Data science, Python, pandas, machine learning, social network analysis, natural language processing | This is a great intermediate introduction to both Python and its use for solving applied data science problems. Taught by UM professors, this specialization has a fairly high bar but features high-quality video and engaging interactive programming and end-of-course assignments that will push you to fully develop your data science skills. | ||||||

14 | 6/13/2017 19:55:02 | xinyutan@umich.edu | https://people.duke.edu/~ccc14/sta-663/index.html | Tutorial/Coding Demo | computational statistics | A very nice introduction to some "advanced" topics which are rarely seen introduced at such an introductory level. | ||||||

15 | 6/13/2017 20:44:54 | thealex@umich.edu | http://karlrosaen.com/ml/ | Webpage | Learning how to learn ML! | This is from a longtime developer who read "The Master Algorithm" and caught the ML bug. He put is VP of Tech / Product Dev / Software Engineering life on hold and took the summer off to study machine learning. He maintained a learning log during his study and ultimately got a job as a Research Engineer in the Ford Autonomous Vehicles Lab. Former MDST regular. | ||||||

16 | 6/13/2017 20:46:04 | thealex@umich.edu | http://distill.pub/ | Publication | Theory presented in a very approachable way. | Incredibly high standards for clarity. Once you know enough about ML to learn fringe concepts, working through these pages can be both enjoyable and enlightening. | ||||||

17 | 6/13/2017 20:54:43 | thealex@umich.edu | https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Tutorial/Coding Demo | Bayesian techniques explored through iPython Notebooks | It's a set of iPython Notebooks! Download them before a long flight and browse at your leisure. I found the section of picking good priors to be especially helpful because I do research on bandit problems. | ||||||

18 | 6/14/2017 8:43:33 | jaredaw@umich.edu | http://karpathy.github.io/2015/05/21/rnn-effectiveness/ | Webpage | Recurrent Neural Networks | Karpathy is an expert on recurrent neural networks and put a lot of time into this explanation of them. The visualizations and examples are simple and effective. This post really helped me understand RNNs. | ||||||

19 | 6/14/2017 9:33:23 | xinyutan@umich.edu | http://www3.canisius.edu/~yany/python/Python4DataAnalysis.pdf | Textbook | pandas, numpy | Pandas is very confusing to me at first. This book is written by Wes McKinney, the main author of the pandas library. He introduces some logics and reasoning of pandas design, making it easier to remember (at least some core functions) and use pandas. | ||||||

20 | 6/16/2017 16:49:49 | stroud@umich.edu | https://www.tensorflow.org/get_started/mnist/beginners | Tutorial/Coding Demo | Tensorflow for beginners, MNIST, Neural Networks | Eases you into the basics of Tensorflow with good figures and examples. Suitable for total beginners. | ||||||

21 | 6/16/2017 16:51:45 | stroud@umich.edu | https://www.tensorflow.org/get_started/mnist/pros | Tutorial/Coding Demo | Tensorflow for ML pros, neural networks | Gentle introduction to CNNs in Tensorflow for people with machine learning experience. Maintained by the Tensorflow team, so it will change as the tools change. | ||||||

22 | 6/16/2017 16:53:10 | stroud@umich.edu | http://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py | Tutorial/Coding Demo | PyTorch, CNNs | Covers all the basics of CNNs in PyTorch. | ||||||

23 | 6/16/2017 16:57:01 | stroud@umich.edu | http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf | Lecture Slides | Deep Learning Software/Hardware | Very in-depth, covers advantages and disadvantages of deep learning frameworks. | ||||||

24 | 6/16/2017 17:00:28 | stroud@umich.edu | http://colah.github.io/posts/2015-08-Understanding-LSTMs/ | Webpage | LSTMs and RNNs | Best tutorial on LSTMs I've ever seen. Really cool figures that get the point across well. | ||||||

25 | 6/16/2017 17:03:36 | stroud@umich.edu | http://distill.pub/2017/momentum/ | Webpage | Momentum (gradient descent) | Interactive figures are super useful and intuitive. | ||||||

26 | 6/16/2017 17:10:17 | stroud@umich.edu | http://playground.tensorflow.org/ | Webpage | Neural Networks | So much fun | ||||||

27 | 6/18/2017 22:48:56 | acell@umich.edu | http://courses.cs.washington.edu/courses/cse512/14wi/ | Washington University Course | data visualization | The website is quite comprehensive, complete with assignments and readings by topic and a resource list that has tools, tutorials, data sets, and links to blogs / other courses. | ||||||

28 | 6/19/2017 11:16:46 | samtenka@umich.edu | http://msail.github.io | Webpage | MSAIL---michigan ML club | Website is terrible. Ugly. Outdated. But the club's pretty fun. | ||||||

29 | 6/19/2017 13:01:29 | pgad@umich.edu | https://www.coursera.org/learn/machine-learning | MOOC | Basic machine learning topics. | Andrew Ng teaches it very well and the MOOC comes with programming exercises. A great starter course. | ||||||

30 | 6/19/2017 13:04:23 | pgad@umich.edu | https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf | Textbook | Convex optimization | Advanced. You can learn about stuff like gradient descent, Newton's algorithm, Lagrange Duals and other stuff used in Machine Learning in great detail. | ||||||

31 | 6/19/2017 13:07:22 | pgad@umich.edu | http://web.eecs.umich.edu/~jabernet/eecs598course/fall2015/web/ | Course notes | Statistical Learning Theory | Advanced. A nice introduction to Statistical Learning Theory. Homeworks available. | ||||||

32 | 6/19/2017 13:56:02 | pgad@umich.edu | http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial | Tutorial/Coding Demo | Unsupervised Feature Learning and Deep Learning | A nice tutorial with coding exercises. | ||||||

33 | 7/12/2017 17:36:43 | pgad@umich.edu | statlect.com | Textbook | Probability and Measure Theory. Formal, rigorous. | Because it is formal and rigorous - Samuel Tenka | ||||||

34 | 8/4/2017 16:59:12 | samtenka@umich.edu | https://jasdeep06.github.io/posts/variable-sharing-in-tensorflow/ | Tutorial/Coding Demo | Variable Sharing in Tensorflow | Finally! A clear and correct explanation! | ||||||

35 | 8/16/2017 1:31:19 | danazhu@umich.edu | https://tspankaj.com/2017/08/14/resources-for-deep-learning/ | Webpage | Deeplearning | Helped me gauge what I needed to do to start getting involved in deep learning. | ||||||

36 | 10/23/2017 16:31:37 | wytian@umich.edu | https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ | Webpage | Convolutional Neural Networks | Wesley - "An incredibly intuitive explanation on how these seemingly mystical things work. Bonus: Lots of pictures and diagrams." | ||||||

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