Chao Wang

[Chao Wang @Wang2Chao]

Thanks for the airdrop, looking forward to seeing what's next for ARB! 🔮🚀 @EQWTDpAJt1pst4R @TrngNgu44802735 @dwi_setia06 @shou_yin11 @AnhLe101188 @cqhnww @hana60633116 @airdropgass @CarlosMija1 @EXuniT007 @igf737 @johan_syah @Quanotis95 @thekera404 @dangnhatkhang

[Chao Wang @Wang2Chao]

@wholemarslog @elonmusk why do you think the taxpayers want you to go to the international space station in the first place, if they can't even pay for their food?

[elvis @omarsar0]

I like the style used by some designers so I customized a few figures and applied a dark-theme because why not. 😻

ML Visuals update:
- Now contains ~50 separate figures/visual components
- Added some dark-theme examples

https://t.co/asSua0NjjY

[Yisong Yue @yisongyue]

Excellent lecture by Felix Berkenkamp on Safe Exploration for Reinforcement Learning and Controller Optimization!

Part of @Caltech class of Data-Driven Algorithm Design.

Video: https://t.co/nbIZtQXezQ

Slides: https://t.co/DsnQfYdjuz

Course: https://t.co/7isXUdGcc6

[Harrison Kinsley @Sentdex]

Heh, sometimes I forget about some of the things we've done as tutorials. I really like this simple env. :D

[Valentine Svensson @vallens]

The single cell studies database has reached 1000 studies https://t.co/pwULbhozyn

[Kirk Borne @KirkDBorne]

Math for #MachineLearning and #Artificialintelligence — FREE 479-page PDF eBook on The #Mathematics and #Statistics Foundations of #DataScience here:
→https://t.co/7lYeig5OhE ←
————
#AI #DeepLearning #BigData #abdsc
———
Also see this math-friendly book: https://t.co/FdPvE6PaOy

[Lior Pachter @lpachter]

The video of "Bioinformatics for Benched Biologists II" (in R) with @LambdaMoses and @sinabooeshaghi is now available at https://t.co/mW3lBjXto4

[Anshul Kundaje @anshulkundaje]

Our BPNet paper been thru 2 review rounds (10 months) now rejected cuz two reviewers appear to be in eternal love with PWMs😂. But wait. U can still read/use/cite it https://t.co/6zU9tYD8Oz! Thank u @biorxivpreprint for existing!

[Bede Portz @YSPTSPS]

A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning: Molecular Cell https://t.co/wm94sKHTiu

[Alfredo Canziani @ ICML @alfcnz]

Week ⑦ videos 🎥 are up, finally! 🎉

Lecture: https://t.co/SfXEK9rhfs
Practicum: https://t.co/5OaUEIdusn
Transcript: https://t.co/70tjGmxKqe

Finally, we got to start the Energy Based Models section! Learn anything about latent variables and autoencoders. Ask Qs on @YouTube.

[Bes D. Marx🔻 @BesDMarx]

#RIPCapitalism

As long as material wealth & control does not belong to and benefit the majority of the people, democracy is nothing but a farce.

[Kirk Borne @KirkDBorne]

This 10-page (PDF) #DataScience Cheat Sheet covers concepts in Statistical Learning, #MachineLearning, #DeepLearning, Probability, #Statistics, #BigData frameworks, SQL, etc.
—>https://t.co/JfnKndlT5Y
——————
#AI #DataScientists #Algorithms #AppliedMathematics #DataMining #abdsc

[Thomas Kipf @tkipf]

My PhD thesis "Deep Learning with Graph-Structured Representations" is now available for download: https://t.co/hyz0cnoewZ -- It covers a range of emerging topics in Deep Learning: from graph neural nets (and graph convolutions) to structure discovery (objects, relations, events)

[MLT & AI Communities @__MLT__]

Learn how to implement a CNN Architecture from a paper:
@dkatsios released a new notebook and the video tutorial for "ShuffleNet: An Extremely Efficient CNN for Mobile Devices".

📚 Notebook: https://t.co/6e0Aoh83X0
🧑‍🏫 Video tutorial: https://t.co/eR6HS3Xd7x

[elvis @omarsar0]

ICYMI, @Harvard is currently offering a great selection of self-paced courses for free.

https://t.co/OsyPrymnUP

[Jason H. Moore, PhD @moorejh]

Open resources for learning and working with the Python programming language https://t.co/FLOYnTH7kN HT @Tomdonoghue #python #coding #programming

[Soumith Chintala @soumithchintala]

ML Code Completeness Checklist: consistent and structured information in the README makes your code more popular and usable.

Sensible advice, backed by data.

Proposed by @paperswithcode and now part of the NeurIPS Code Submission process.

Read more: https://t.co/ev4Hr8SCVU

[elvis @omarsar0]

This playlist of 100+ videos (on models, inferencing, and algorithms) includes a nice set of random talks to add to your #onlinesemester:
- geometric DL
- Bayesian DL
- large-scale neuroscience
- extracting causal signals
- multitask learning
- AutoML
..

https://t.co/givwpE0k8o

[Xiaole Shirley Liu @XShirleyLiu]

Gad Getz gave a very comprehensive lecture on Cancer Genome Analysis today (7 short videos: https://t.co/ZoAaI42SFO, https://t.co/8Z5EFelRY6, https://t.co/745btY3fdd, https://t.co/LtA6Cpujbk, https://t.co/ZW1ZgdlRWV, https://t.co/o1oSSFDoQM, https://t.co/JkLgQCqojS) Enjoy!