✨✨ BIG NEWS: We are hiring!! ✨✨
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩
👇 Here below a thread on who we are and what we
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines
https://t.co/1KPERqj7If
Hi everyone! I'm Louise Bowler, a Research Data Scientist from @turinginst's Research Engineering Group @turinghut23. I'm borrowing the account for the day to show you all a day in the life of a Research Data Scientist! \U0001f469\u200d\U0001f4bb
— Research Engineering at the Turing (@turinghut23) March 10, 2020
https://t.co/KknkBtl6bg / @CamilaRangelS @radka_jersak @louise_a_bowler
Today this account is being taken over by Kevin Xu, from the civil service fast stream program that is doing a placement in our team for the next 6 months. He will be talking about the projects he is working on and how is to join a new team fully remote. pic.twitter.com/3ULLODPuDV
— Research Engineering at the Turing (@turinghut23) July 1, 2020
https://t.co/PaYng3c5Qa
Today it\u2019s \U0001f389 graduation time \U0001f389 for @openlifesci. @CamilaRangelS Sam Van Stroud @Kevinzhangxu and myself have worked so hard to get here with @TuringDStories \U0001f4ac\U0001f4ca\U0001f4c8\U0001f9e0. If you want to learn how to maximise your open research, then apply for the next cohort https://t.co/t7GUZxP9Fl
— David Beavan (@DavidBeavan) December 15, 2020
https://t.co/0jPjUXvtOu
https://t.co/dgkK45JzN6 #HPC
📨 https://t.co/3e4b0dsEDJ
👉 https://t.co/LnGW7JNQX5
More from Data science
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".
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)
📘 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.
📘 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.
📘 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,...
📘 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)
📘 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.
📘 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.
📘 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,...
📘 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.
To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇
https://t.co/EwwOzgfDca : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks.
https://t.co/J4qMzPAZ6u Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs).
https://t.co/9IgKkSxPCq a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.
https://t.co/EAqn2YngIE : TensorFlow Java API (experimental)
https://t.co/EwwOzgfDca : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks.
https://t.co/J4qMzPAZ6u Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs).
https://t.co/9IgKkSxPCq a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.
https://t.co/EAqn2YngIE : TensorFlow Java API (experimental)
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1/Politics thread time.
To me, the most important aspect of the 2018 midterms wasn't even about partisan control, but about democracy and voting rights. That's the real battle.
2/The good news: It's now an issue that everyone's talking about, and that everyone cares about.
3/More good news: Florida's proposition to give felons voting rights won. But it didn't just win - it won with substantial support from Republican voters.
That suggests there is still SOME grassroots support for democracy that transcends
4/Yet more good news: Michigan made it easier to vote. Again, by plebiscite, showing broad support for voting rights as an
5/OK, now the bad news.
We seem to have accepted electoral dysfunction in Florida as a permanent thing. The 2000 election has never really
To me, the most important aspect of the 2018 midterms wasn't even about partisan control, but about democracy and voting rights. That's the real battle.
2/The good news: It's now an issue that everyone's talking about, and that everyone cares about.
3/More good news: Florida's proposition to give felons voting rights won. But it didn't just win - it won with substantial support from Republican voters.
That suggests there is still SOME grassroots support for democracy that transcends
4/Yet more good news: Michigan made it easier to vote. Again, by plebiscite, showing broad support for voting rights as an
5/OK, now the bad news.
We seem to have accepted electoral dysfunction in Florida as a permanent thing. The 2000 election has never really
Bad ballot design led to a lot of undervotes for Bill Nelson in Broward Co., possibly even enough to cost him his Senate seat. They do appear to be real undervotes, though, instead of tabulation errors. He doesn't really seem to have a path to victory. https://t.co/utUhY2KTaR
— Nate Silver (@NateSilver538) November 16, 2018
