You remember prime numbers, right? Those numbers you can’t divide into other numbers, except when you divide them by themselves or 1? Right. Here is a 3000 year old question: Present an argument or…
Hi Guys, I have Always been asked to share my code which I use in my video. Answering people’s questions is great, and the feeling you get when you solve a p...
ReSanskrit explores every aspect of Aditya Hrudayam Stotram - right from the story, rules of recitation, benefits and scientific significant. Click to read now
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
An attempt to create a convenient workspace that makes it possible to work with multiple custom python libraries, while keeping all benefits of Google Colaboratory.
- Aug. 31 – Sep. 4, 2020
- Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)
A. Slivkins. (2019)cite arxiv:1904.07272Comment: The manuscript is complete, but comments are very welcome! To be published with Foundations and Trends in Machine Learning.
R. Sharipov. (2004)cite arxiv:math/0412421Comment: The textbook, AmSTeX, 132 pages, amsppt style, prepared for double side printing on letter size paper.
R. Sharipov. (2004)cite arxiv:math/0405323Comment: The textbook, AmSTeX, 143 pages, amsppt style, prepared for double side printing on letter size paper.