✨✨ 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
/ @DavidBeavan @LivingwMachines
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
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
More from Data science
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
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.
2/ In this gif, narrow relu networks have high probability of initializing near the 0 function (because of relu) and getting stuck. This causes the function distribution to become multi-modal over time. However, for wide relu networks this is not an issue.
3/ This time-evolving GP depends on two kernels: the kernel describing the GP at init, and the kernel describing the linear evolution of this GP. The former is the NNGP kernel, and the latter is the Neural Tangent Kernel (NTK).
4/ Once we have these two kernels, we can derive the GP mean and covariance at any time t via straightforward linear algebra.
5/ So it remains to calculate the NNGP kernel and NT kernel for any given architecture. The first is described in https://t.co/cFWfNC5ALC and in this thread
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)
(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.
(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).
(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.
(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.
Some thoughts worked out in a letter to a friend, which is the kind of thing you do when off Twitter for a glorious week. (🧵)
“Chance is ignorance”—the Bayesian story; all probabilities represent states of mind, not states of the world. One *could* put (some) chances “in the world”, but let’s take Occam’s Razor seriously...
That the probability of a fair coin coming up heads is 50% simply means that marginalizing (tracing, as the physicists say) over the hidden facts leaves you, nearly, maximally ignorant of the outcome.
Quantum uncertainty (access below!) poses an apparent challenge to this story. There seems to be nothing to be ignorant about when it comes to (say) electron spin—there is nothing “inside” the
The electron is a simple object, in other words. So where does the uncertainty come from? One could follow David Wallace’s wonderful interpretation in terms of chaotic dynamics and decoherence, but let’s see if we can take another route...
You May Also Like
Here's how I'd measure the health of any tech company:— Jeff Atwood (@codinghorror) October 25, 2018
How long, as measured from the inception of idea to the modified software arriving in the user's hands, does it take to roll out a *1 word copy change* in your primary product?
How long does it take, measured from initial expression of interest through offer of employment signed, for a typical candidate cold inbounding to the company?
What is the *theoretical minimum* for *any* candidate?
How long does it take, as a developer newly hired at the company:
* To get a fully credentialed machine issued to you
* To get a fully functional development environment on that machine which could push code to production immediately
* To solo ship one material quanta of work
How long does it take, from first idea floated to "It's on the Internet", to create a piece of marketing collateral.
(For bonus points: break down by ambitiousness / form factor.)
How many people have to say yes to do something which is clearly worth doing which costs $5,000 / $15,000 / $250,000 and has never been done before.
Let folks have their many talents, interests and gifts. Life is far more fun with variety, loves.
A lot of folks have come to know me as an activist & I’m grateful that folks care to know me at all.
But I wasn’t born in 2014. I was a whole teacher, executive, policy person, speaker, arts and culture lover, reader, writer, woman of faith, fashion and more before 4 yrs ago 🤷🏾♀️
We rightfully complain that marginalized people are not allowed to be fully human.
But we internalize and transfer our oppression daily. It’s a smog. We all breathe it in & act it out.
And then tell WoC “girl ain’t you supposed to be a _______? Why you doing ____?”
Can I live?
And don’t go reading anything personal into this-this isn’t about me necessarily and it’s no subtweet (I try hard not to do that.)
I’ve just been observing that behavior more and more lately. Especially when it comes to marginalized folks.
Evolution should be our aspiration.
“Can’t knock the hustle” should be our anthem.
As long as someone isn’t bringing active and continual harm, why can’t they explore their many sides?
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.
2. Marvel Comics (before that Atlas) was just a cog in the machine of a bottom pulp publisher run by Martin Goodman, the husband of one of Lee's cousins. It was the lowest of the low in the publishing world.
3. Now Mario Puzo (not yet the author of the Godfather) shared offices with Stan Lee in the 1950s and 1960s. Puzo wrote for garrish men's adventure magazines and, like Lee, dreamed of writing a novel & breaking out. But Puzo looked down on Lee.
4. Flo Steinberg, 1960s secretary at Marvel: "They were always making jokes about us. They'd come in and giggle. mario Puzo would look in and would see us all working on his way to the office and say, 'Work faster, little elves. Christmas is coming.'"
5. When JFK was killed, the whole office of Magazine Management was stunned and quiet. Except Lee. He continued working. "He was still working on the comic books," Puzo said. "Like that was the most important thing in the world."
studies since March 2020. Below are 30 published papers finding that lockdowns had little or no efficacy (despite unconscionable harms) along with a key quote or two from each:
“there is no evidence that more restrictive nonpharmaceutical interventions (“lockdowns”) contributed substantially to bending the curve of new cases in England, France, Germany, Iran, Italy, the Netherlands, Spain, or the United States in early 2020”
“Inferences on effects of NPIs are non-robust and highly sensitive to model specification. Claimed benefits of lockdown appear grossly exaggerated.”
“government actions such as border closures, full lockdowns, and a high rate of COVID-19 testing were not associated with statistically significant reductions in the number of critical cases or overall mortality”