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Letās talk about science and research for a second. Research journals are given a rating used to measure their importance based on citations. This is called an āImpact Factorā. An Impact Factor of >10 puts a journal near the top 1% of all cited journals. 1/x
So a high Impact Factor journal is widely considered reputable by the general scientific/research/medical community. So when a high Impact Factor journal comes out with a statement, it should be taken seriously. 2/x
Consider this article āTrump lied about scienceā from @ScienceMagazine (IF 41.85) https://t.co/6ApeVqxgWO 3/x
Consider this article āWhy Nature supports Joe Biden for US Presidentā from @Nature (IF 42.778) https://t.co/2AJx5A3zUU 4/x
Consider this article āTrump versus Biden: a fight for the health of a nationā from @TheLancet (IF 60.392) https://t.co/tfbZ1LAnM2 5/x
So a high Impact Factor journal is widely considered reputable by the general scientific/research/medical community. So when a high Impact Factor journal comes out with a statement, it should be taken seriously. 2/x
Consider this article āTrump lied about scienceā from @ScienceMagazine (IF 41.85) https://t.co/6ApeVqxgWO 3/x

Consider this article āWhy Nature supports Joe Biden for US Presidentā from @Nature (IF 42.778) https://t.co/2AJx5A3zUU 4/x

Consider this article āTrump versus Biden: a fight for the health of a nationā from @TheLancet (IF 60.392) https://t.co/tfbZ1LAnM2 5/x

20 years on, #BushVGore is back in our discourse - an election stolen by mobs at the polling places, a media blitz, and a Supreme Court at its most antidemocratic and antimajoritarian.
1/
We remember this as an election that the plutes stole, but it's also an election that the Dems gave to them. That's why we're talk about it now. There will be an attempt to steal next month's election. Will we surrender again?
2/
The last surrender led to a war being fought today by the children of the soldiers who were sent into battle on day one. It led to climate inaction, monopolistic concentration, erosions to our right to vote and to our right to protest.
3/
Thanks to that surrender, voter suppression was expanded and reinforced, leading to the election of a fumbling liar who got to appoint more SCOTUS judges in 3.5 years than all Democratic presidents did in the previous quarter
You may be wondering how the Dems surrendered in 2020. For a detailed account of what surrender looks like, read @rsgexp's @jacobin memoir of her time as an @AFLCIO organizer sent to Florida during the
1/

We remember this as an election that the plutes stole, but it's also an election that the Dems gave to them. That's why we're talk about it now. There will be an attempt to steal next month's election. Will we surrender again?
2/
The last surrender led to a war being fought today by the children of the soldiers who were sent into battle on day one. It led to climate inaction, monopolistic concentration, erosions to our right to vote and to our right to protest.
3/
Thanks to that surrender, voter suppression was expanded and reinforced, leading to the election of a fumbling liar who got to appoint more SCOTUS judges in 3.5 years than all Democratic presidents did in the previous quarter
If Amy Barrett is confirmed, Donald Trump alone will have appointed the same number of SCOTUS judges in 3-and-a-half years as Democratic presidents have appointed in the last 26 years.
— David Sirota (@davidsirota) October 13, 2020
So please -- stop being afraid to talk about court expansion. https://t.co/dZU97YhFWg
You may be wondering how the Dems surrendered in 2020. For a detailed account of what surrender looks like, read @rsgexp's @jacobin memoir of her time as an @AFLCIO organizer sent to Florida during the
I have always emphasized on the importance of mathematics in machine learning.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
š Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
š Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
š The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
š Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
š Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.

š Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.

š The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...

š Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
Wellll... A few weeks back I started working on a tutorial for our lab's Code Club on how to make shitty graphs. It was too dispiriting and I balked. A twitter workshop with figures and code:
Here's the code to generate the data frame. You can get the "raw" data from https://t.co/jcTE5t0uBT
Obligatory stacked bar chart that hides any sense of variation in the data
Obligatory stacked bar chart that shows all the things and yet shows absolutely nothing at the same time
STACKED Donut plot. Who doesn't want a donut? Who wouldn't want a stack of them!?! This took forever to render and looked worse than it should because coord_polar doesn't do scales="free_x".
When are you doing pie charts?
— #BlackLivesMatter (@surt_lab) October 13, 2020
Here's the code to generate the data frame. You can get the "raw" data from https://t.co/jcTE5t0uBT

Obligatory stacked bar chart that hides any sense of variation in the data

Obligatory stacked bar chart that shows all the things and yet shows absolutely nothing at the same time

STACKED Donut plot. Who doesn't want a donut? Who wouldn't want a stack of them!?! This took forever to render and looked worse than it should because coord_polar doesn't do scales="free_x".
