Ryan Rodriguez 🇺🇦

[Ryan Rodriguez 🇺🇦 @RyRodri]

@kvlly Dropbox: https://t.co/bJgxc82UUp

[Sridhar Ramesh @RadishHarmers]

LaMDA isn't sentient, according to the widely-accepted argument of John Searle. It speaks English, whereas a sentient being would speak Chinese.

[Mike Tamir, PhD @MikeTamir]

TheAlgorithms/Python All Algorithms implemented in Python. https://t.co/bmpEvskMAf
#AI #DeepLearning #MachineLearning #DataScience

[Mario Cervera @macerub]

✏️ Interesting fact about Python:

Variables are always pointers to objects, not labels of changeable memory areas.

Strictly speaking, when you set a variable to a new value, you don't modify the value of the variable.

The variable just references a different object.
👇🧵1/2

[Dickie Bush @dickiebush]

If you use a Mac and your application dock still takes up space on your screen, this thread will change your life:

[Dickie Bush @dickiebush]

If you use it right, Twitter is the most powerful platform in the world.

But Twitter does a horrible job of showing you its advanced features.

Here are 10 of them you probably know nothing about:

[Santiago @svpino]

Looking for kick-ass, 100% free machine learning courses?

- MIT 6.S191 Introduction to Deep Learning
- DS-GA 1008 Deep Learning
- UC Berkeley Full Stack Deep Learning
- UC Berkeley CS 182 Deep Learning
- Cornell Tech CS 5787 Applied Machine Learning

Find them and pick one.

[Derek Thompson @DKThomp]

I wrote about how mRNA technology could change the world

https://t.co/DiWxZFCEpK

Here are 5 favorite details from the piece

[Google DeepMind @GoogleDeepMind]

Why does Stochastic Gradient Descent generalise well in deep networks?

Our team shows that if the learning rate is small but finite, the mean iterate of random shuffling SGD stays close to the path of gradient flow, but on a modified loss landscape https://t.co/JUAzPujWfP

[Santiago @svpino]

Applied Machine Learning.
Cornell Tech, 2020.
100% Free.
80 videos.

Here is the full playlist: https://t.co/hD5no8Pdfa

[Santiago @svpino]

You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

[Jeremy Howard @jeremyphoward]

This is the clearest and most correct explanation of threads and processes I've seen in Python.

All coders (including data scientists) need to understand this important topic.

[Jordan Ellenberg @JSEllenberg]

On arXiv today: @mrtz and @beenwrekt's new textbook on machine learning. I learned a TON from this. https://t.co/vJdVMiTjJU

[Chip Huyen @chipro]

So I wrote a 5400-word lecture note on the basics of data engineering for my students, covering:

* data formats (row- vs. column-based, text vs. binary)
* ETL
* batch processing vs. stream processing
* training datasets

WIP. Feedback much appreciated!

https://t.co/oFdTDSIAin

[Natalie E. Dean, PhD @nataliexdean]

Oxford/AZ reports overall reduction in PCR positivity of 54.1%, but only 2% "vaccine efficacy against asymptomatic infection."

Confused?

Allow me to explain with crudely drawn pictures why the overall findings are still quite positive. 1/8

[Thomas Wiecki @twiecki]

Primer on Bayesian statistics published in Nature. But it's not only science that is recognizing Bayesian modeling as the powerful inference method that it is, I see the same happening in business. Exciting times! https://t.co/TiIgD3TK5C

[Jeremy Howard @jeremyphoward]

Wow.

@OpenAI has trained a model that can create new images - you just describe them in English. 🤯

https://t.co/qxDreBM2Qx

[Amin Karbasi @aminkarbasi]

Here is the list of 20 papers I read, enjoyed, and learned from. What is your 2020 list?

[François Chollet @fchollet]

Check out this excellent step-by-step demonstration of how you can develop a TF/Keras project in a Kaggle notebook and send it to Google Cloud for training at scale right from your notebook, using TF Cloud
https://t.co/TeRzeWwPpX

[Edgar Dobriban @EdgarDobriban]

Excited to discuss our #NeurIPS2020 oral presentation on data augmentation! Want to know why data augmentation helps improve performance of #ml methods? See work at https://t.co/CVJYf4csmf, and pls share! 1/