BC DATA SCIENCE

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Maria Khalusova
@mariaKhalusova 4 years, 4 months ago 787 views

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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/7TY0viBfF5: ML algorithms, feature preprocessing and pipelines. Scalable through distributed computations.

https://t.co/9EVdIXwJuo: The toolkit for common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, coreference resolution, language detection and more!

https://t.co/AnxgGmsux2: distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.

https://t.co/fiexCElwRp : Statistical Machine Intelligence and Learning Engine: classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, nearest neighbor search..

https://t.co/kDGCjszAaA Kotlin∇ is a type-safe automatic differentiation framework in Kotlin. It allows users to express differentiable programs with higher-dimensional data structures and operators.

(Not yet released) automatic differentiation system for the Kotlin language: https://t.co/9ANDDIVW8o

https://t.co/jKeboC2z0V open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers.

https://t.co/pXkvxumzrw - a set of simple, scalable and efficient tools that allow the building of predictive Machine Learning models without costly data transfers.

More from Data science

Research Engineering a...
@turinghut23

✨✨ 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

$Sunrit Jana \U0001f680$

Sunrit Jana 🚀
@JanaSunrise

Pandas is an amazing data analysis and manipulation library for python, Really popular when working with ML, Data science or more.

It has a robust data structure, Dataframe for manipulation and analyzing data.

Here's some tips to help you work better with pandas. Let's go! ↓

If you're not aware about what a Dataframe is, It's an optimized data structure for loading data, analysing it, manipulating data in it, and Mostly gathering insights.

It uses Cython backend which transpiles into C for optimized code.

Here's how a dataframe looks like,

Before we start, You need to ensure, you have pandas installed. If you don't, Do that before moving ahead!

Here are the tips, Let's go!

1/ Convert PD series to Dataframe

We all have struggled, when we deal with pandas series. It's always easier to work with Dataframes, rather than series. Here is how you can convert series to dataframe easily.

2/ How to create dummy Dataframe for testing

We always need dataframes for testing and analysing normally, if we do not have data ready. Here is how you can use Pandas API to generate different types of data.

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John Myles White
@johnmyleswhite

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(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.

Pat Schloss
@PatSchloss

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:

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".

Ryan J. Gallagher
@ryanjgallag

Tired of word clouds? Want to do better sentiment analysis? Not sure how to look at the words underneath your measures?

Our long overdue paper on generalized word shift graphs is finally here!
https://t.co/lIBXvbMJWX
https://t.co/vSL1REYT8V

So what are they?

1/n

If we have two texts, there are many ways we can compare them. Weighted averages are a particularly useful measure because they're flexible and interpretable

Proportions, Shannon entropy, the KLD, the JSD, and dictionary methods can all be written as weighted averages

2/n

But weighted avgs are also slippery. When we try to compress complex phenomena like happiness, surprise, divergence, or diversity into a single number, it can be unclear what we're measuring

If the measure goes up, what does that mean? Why did it do that? Can we trust it?

3/n

Very often, that's the end of the line and we're left with an uneasy feeling in the pit of our stomach that our weighted avg is actually picking up a data artifact or some other unintended peculiarity

Word shift graphs help us address those concerns

4/n

First, word shifts look under the hood of weighted averages to see what's going on

All weighted averages are a sum of contributions from individual words. We can pull out those words, and rank which ones contribute the most to the difference between two texts

5/n

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Suchi Sharma
@Suchi_sharma_

#THREAD
1.Meenakshi Amman Temple(472ft High)also called as Meenakshi Sundareswarar is a famous Hindu temple located on the southern bank of Vaigai River in temple city of Madurai, #TamilNadu.It is dedicated to Parvati(Meenakshi)& her consort Shiva(Sundareswarar). @LostTemple7

2.Madurai Meenakshi Sundareswarar temple was built by King Kulasekara Pandya (1190-1216 CE).This popular temple visits over 15000 visitors daily & around 25000 on #Friday !Friday sees the most number of devotees visit the temple with the number going as high as +25,000.

3.Meenakshi Temple also hosts an annual festival called Meenakshi Thirukalayanam that is held in April. A celestial wedding of the god and goddess marks the end of the festival. About 1 million devotees visit the temple during this festival.

4.the temple has been tagged as one of the seven wonders of India along with stunning Architecture.The temple is built using Dravidian South Indian style of architecture, and is one of the most impressive temple structures in South India.

5.The principle deity here is MEENAKSHI, a form of Goddess Parvati. The idol of the goddess is carved in emerald hued stone. The Meenakshi idol has 3 breasts as Meenakshi was born with three breasts and was blessed that the 3rd breast would vanish once meets right man of her life

William
@wimgz

🛠️ OPEN BUSINESS IDEAS 🛠️

Below a list of business/product ideas I had or read about.

They are worthless if they remain ideas and if you don't overcome the challenges in building them, so feel free to copy / tweak / implement them!
👇

Better: use one and make a MVP during the #24hrstartup challenge!

😻 Product Hunt Time
A clock that displays the time it is @ProductHunt
Also displays what you should do and where you should post, at each specific time during your launch

🚧 IndieCrunch - VC free tech news
Techcrunch but only for bootstrapped companies

🎧 Kickstarter for audiobooks
A lot of awesome books are not available as audio.
Crowdfund the money to buy the audio rights + a voice actor

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Seth Abramson
@SethAbramson

(THREAD) Pre-election Russia collusion may take down Trump. It's equally possible the Trumps' pre- and post-election collusion with Saudi Arabia, the UAE, and Israel will do it—a course of collusion also connected to the Kremlin. I explain here. I hope you'll read on and retweet.

1/ I must stress how unbelievably complex the "Grand Bargain" theory of the Trump-Russia case is—a different thing from saying it's not substantiated. It's substantiated in *almost every single particular*—it just *also* happens to be very confusing. Not byzantine—just confusing.

2/ The basics: Saudi Arabia, Israel, and the UAE all view Iran as their chief regional enemy. Iran is propped up by Russia. Therefore the Saudis, Israelis, and Emiratis all need a US government willing to find a way to get the Kremlin to *stop* supporting Iran in the Middle East.

3/ The best way to get Russia to stop supporting Iran—or reduce support—was/is to drop all sanctions on Russia over its 2014 annexation of Crimea, as that'd be worth *trillions* to the Kremlin over the next decade. Everyone knew that Clinton wouldn't do this—and that Trump would.

4/ Per the NYT, on August 3, 2016, Donald Trump Jr. met secretly at Trump Tower with a Saudi and Emirati emissary, George Nader, as well as an Israeli intelligence expert, Joel Zamel, with *significant* ties to both Israeli intelligence *and* Russian oligarchs allied with Putin.

John Myles White
@johnmyleswhite

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(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.

Chuck Wendig
@ChuckWendig

TRUMP: "I've decided to eat babies."

PEOPLE: "He can't eat babies, that's super illegal."

TRUMP, on TV, eating babies, not even cooking them first: "People are saying that I really am the best baby-eater, folks."

NYT: "Trump Vs. Babies: The Rhetoric On Both Sides Must Stop"

Jeff Flake tweets: "It is a sad day in America when this callous man can eat babies live on TV"

Jeff Flake, 10 seconds later, votes to help him install a series of 4,000 new judges that are very, very pro-babyphagia.

Judge Kavanaugh, found drunk in a Capitol Hill closet: "I LIKE BEER. OKAY? I LIKE BEER AND I LIKE BABIES AND THAT'S NOT SO WRONG. THAT'S RIGHT. I LIKE BABIES DIPPED IN BEER. I JUST DIP EM. IN THE BEER. THE BABIES. THE BABIES I EAT. OKAY? THAT'S AMERICA."

Mike Pence, wringing his hands about the baby-eating: "We must take care of our children. Our youngest must be protected on this troubling day."

Mike Pence then invites a Baby Chef to say a prayer.

Twitter Users: "It's a distraction technique! Trump eating babies is trying to DISTRACT YOU from ALL THE OTHER HORRORS, which are themselves distractions from the BABY-EATING. It's DISTRACTIONS ALL THE WAY DOWN."

Trump: *eats tons of babies at a rally*

*literal tons of babies*