Foundations of Machine Learning by Bloomberg, a training course that was initially delivered internally to the company's software engineers (30 videos):https://t.co/zJMyROIxEM
β fastml extra (@fastml_extra) July 15, 2018
Foundations of Machine Learning by Bloomberg, a training course that was initially delivered internally to the company's software engineers (30 videos):https://t.co/zJMyROIxEM
β fastml extra (@fastml_extra) July 15, 2018
Here is the video for my keynote from #useR2018 on teaching R to new users. https://t.co/KUrG097D7D
β Roger D. Peng (@rdpeng) July 15, 2018
ICYMI, another π git guide (by proj type):
β Mara Averick (@dataandme) July 15, 2018
βA Quick Intro to Version Control w/ Git & GitHubβ by @jdblischak @emo_davenport & @gvwilsonhttps://t.co/CAPlGZjZm9 #git #github pic.twitter.com/SG26XoPcki
I just found βIntro to AIβ _very_ nicely drawn video course by Dan Klein and @pabbeel. This is _really_ eyes and brain candies! I cannot wait to dig in and learn how to be a better teacher!!! *feels absolutely excited* https://t.co/jq78usCdDp
β Alf (ε·ε¨) (@AlfredoCanziani) July 15, 2018
A few drawings from the MDP section. pic.twitter.com/K3Xo68uq3X
Today, at #scipy2018 sprints, I learned that not enough folks know about `git grep`
β Paul βΟβ Ivanov (@ivanov) July 14, 2018
Search the current checkout of only committed files, ignores other stuff. pic.twitter.com/1bZbZFQ5T7
Awesome #datascience videos on our #PyData keynote playlist, including:
β PyData (@PyData) July 14, 2018
"Making the Big Data ecosystem work together with Python" by Holden Karau β on the work being done to decrease the overhead of #bigdata in #Pythonhttps://t.co/qvIvKnDoJL
Subscribe to our channel for more!
Tip #8: Want to speed up your code? First, identify the bottleneck. Profiler is your friend. In #Python, use cProfile (https://t.co/cSnFEnDiWx). pic.twitter.com/UPIV6zjWvH
β Jeong-Yoon Lee (@jeongyoonlee) July 14, 2018
A very comprehensive paper for those into kernel methods and GPs.
β Neil Lawrence (@lawrennd) July 14, 2018
Gives deep insights into the nature of our assumptions when modelling.
Thanks Motonobu Kanagawa, Philipp Hennig, @sejDino and Bharath K Sriperumbudur!https://t.co/UfNBoSHixu
Slides of my talk at #ICML2018 on reproducible #MachineLearning workshop https://t.co/8PCk5cZlnJ https://t.co/cH3PhRZz4K #RML2018 @scikit_learn #opensource #openscience pic.twitter.com/CU22oMgsoS
β Alexandre Gramfort (@agramfort) July 14, 2018
My talk at #SciPy2018 on Multithreading and Multiprocessing in Python is now viewable on Youtube!https://t.co/uv9lVww8Fb
β David Liu (@triskadecaepyon) July 13, 2018
There are some fantastic links on neural network acceleration hardware in this CS217 syllabus: https://t.co/f056QIt2AT - thanks @perdavan and Kunle Olukotun!
β Pete Warden (@petewarden) July 13, 2018
A user just submitted an example visualization to Altair that I had no idea was even possible https://t.co/hZyh2WXHja pic.twitter.com/rIZj2S6EFS
β Jake VanderPlas (@jakevdp) July 13, 2018