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

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".
✨✨ 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

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First thread of the year because I have time during MCO. As requested, a thread on the gods and spirits of Malay folk religion. Some are indigenous, some are of Indian origin, some have Islamic


Before I begin, it might be worth explaining the Malay conception of the spirit world. At its deepest level, Malay religious belief is animist. All living beings and even certain objects are said to have a soul. Natural phenomena are either controlled by or personified as spirits

Although these beings had to be respected, not all of them were powerful enough to be considered gods. Offerings would be made to the spirits that had greater influence on human life. Spells and incantations would invoke their


Two known examples of such elemental spirits that had god-like status are Raja Angin (king of the wind) and Mambang Tali Arus (spirit of river currents). There were undoubtedly many more which have been lost to time

Contact with ancient India brought the influence of Hinduism and Buddhism to SEA. What we now call Hinduism similarly developed in India out of native animism and the more formal Vedic tradition. This can be seen in the multitude of sacred animals and location-specific Hindu gods