I created these by hand, based on excellent investigate journalism:
This is a more wonky thread about how I made this visualization in #Rstats using the awesome visNetwork
I created these by hand, based on excellent investigate journalism:
You'll notice that I included a column for "type" in the nodes file. This allows me to use different icons for people vs firms vs political organizations.
Unsurprisingly, this means that the UK government and the Conservative party emerge as the most connected nodes in this network!
You can add pop-up boxes ("tool-tips") that show more information when the user hovers over a node or edge – perfect for linking to the original reporting that I used.
More from Data science
✨✨ 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
We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩💻 presentation for some examples:
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
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
We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩💻 presentation for some examples:
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
1/ A ∞-wide NN of *any architecture* is a Gaussian process (GP) at init. The NN in fact evolves linearly in function space under SGD, so is a GP at *any time* during training. https://t.co/v1b6kndqCk With Tensor Programs, we can calculate this time-evolving GP w/o trainin any NN
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
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
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".
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Trending news of The Rock's daughter Simone Johnson's announcing her new Stage Name is breaking our Versus tool because "Wrestling Name" isn't in our database!
Here's the most useful #Factualist comparison pages #Thread 🧵
What is the difference between “pseudonym” and “stage name?”
Pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie stars,” while stage name is “the pseudonym of an entertainer.”
https://t.co/hT5XPkTepy #english #wiki #wikidiff
People also found this comparison helpful:
Alias #versus Stage Name: What’s the difference?
Alias means “another name; an assumed name,” while stage name means “the pseudonym of an entertainer.”
https://t.co/Kf7uVKekMd #Etymology #words
Another common #question:
What is the difference between “alias” and “pseudonym?”
As nouns alias means “another name; an assumed name,” while pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie
Here is a very basic #comparison: "Name versus Stage Name"
As #nouns, the difference is that name means “any nounal word or phrase which indicates a particular person, place, class, or thing,” but stage name means “the pseudonym of an
Here's the most useful #Factualist comparison pages #Thread 🧵
What is the difference between “pseudonym” and “stage name?”
Pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie stars,” while stage name is “the pseudonym of an entertainer.”
https://t.co/hT5XPkTepy #english #wiki #wikidiff
People also found this comparison helpful:
Alias #versus Stage Name: What’s the difference?
Alias means “another name; an assumed name,” while stage name means “the pseudonym of an entertainer.”
https://t.co/Kf7uVKekMd #Etymology #words
Another common #question:
What is the difference between “alias” and “pseudonym?”
As nouns alias means “another name; an assumed name,” while pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie
Here is a very basic #comparison: "Name versus Stage Name"
As #nouns, the difference is that name means “any nounal word or phrase which indicates a particular person, place, class, or thing,” but stage name means “the pseudonym of an
