|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)
Machine Learning: A Probabilistic Perspective (available on Amazon/in the Library)
General topics in Machine Learning
He explains all the topics he covers really well, and gives great statistical perspective.
|6/1/2017 20:52:firstname.lastname@example.org||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.
|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.
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.
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.
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.
|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.
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.
Turned me on to Recurrent Neural Nets
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.
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.
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.
A very nice introduction to some "advanced" topics which are rarely seen introduced at such an introductory level.
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.
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.
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.
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.
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.
Tensorflow for beginners, MNIST, Neural Networks
Eases you into the basics of Tensorflow with good figures and examples. Suitable for total beginners.
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.
|Tutorial/Coding Demo||PyTorch, CNNs|
Covers all the basics of CNNs in PyTorch.
Deep Learning Software/Hardware
Very in-depth, covers advantages and disadvantages of deep learning frameworks.
|Webpage||LSTMs and RNNs|
Best tutorial on LSTMs I've ever seen. Really cool figures that get the point across well.
Momentum (gradient descent)
Interactive figures are super useful and intuitive.
|Webpage||Neural Networks||So much fun|
Washington University Course
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.
MSAIL---michigan ML club
Website is terrible. Ugly. Outdated. But the club's pretty fun.
Basic machine learning topics.
Andrew Ng teaches it very well and the MOOC comes with programming exercises. A great starter course.
Advanced. You can learn about stuff like gradient descent, Newton's algorithm, Lagrange Duals and other stuff used in Machine Learning in great detail.
Statistical Learning Theory
Advanced. A nice introduction to Statistical Learning Theory. Homeworks available.
Unsupervised Feature Learning and Deep Learning
A nice tutorial with coding exercises.
Probability and Measure Theory. Formal, rigorous.
Because it is formal and rigorous - Samuel Tenka
Variable Sharing in Tensorflow
Finally! A clear and correct explanation!
Helped me gauge what I needed to do to start getting involved in deep learning.
Convolutional Neural Networks
Wesley - "An incredibly intuitive explanation on how these seemingly mystical things work. Bonus: Lots of pictures and diagrams."