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

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.
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".

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दधीचि ऋषि को मनाही थी कि वह अश्विनी कुमारों को किसी भी अवस्था में ब्रह्मविद्या का उपदेश नहीं दें। ये आदेश देवराज इन्द्र का था।वह नहीं चाहते थे कि उनके सिंहासन को प्रत्यक्ष या परोक्ष रुप से कोई भी खतरा हो।मगर जब अश्विनी कुमारों ने सहृदय प्रार्थना की तो महर्षि सहर्ष मान गए।


और उन्होनें ब्रह्मविद्या का ज्ञान अश्विनि कुमारों को दे दिया। गुप्तचरों के माध्यम से जब खबर इन्द्रदेव तक पहुंची तो वे क्रोध में खड़ग ले कर गए और महर्षि दधीचि का सर धड़ से अलग कर दिया।मगर अश्विनी कुमार भी कहां चुप बैठने वाले थे।उन्होने तुरंत एक अश्व का सिर महर्षि के धड़ पे...


...प्रत्यारोपित कर उन्हें जीवित रख लिया।उस दिन के पश्चात महर्षि दधीचि अश्वशिरा भी कहलाए जाने लगे।अब आगे सुनिये की किस प्रकार महर्षि दधीचि का सर काटने वाले इन्द्र कैसे अपनी रक्षा हेतु उनके आगे गिड़गिड़ाए ।

एक बार देवराज इन्द्र अपनी सभा में बैठे थे, तो उन्हे खुद पर अभिमान हो आया।


वे सोचने लगे कि हम तीनों लोकों के स्वामी हैं। ब्राह्मण हमें यज्ञ में आहुति देते हैं और हमारी उपासना करते हैं। फिर हम सामान्य ब्राह्मण बृहस्पति से क्यों डरते हैं ?उनके आने पर क्यों खड़े हो जाते हैं?वे तो हमारी जीविका से पलते हैं। देवर्षि बृहस्पति देवताओं के गुरु थे।

अभिमान के कारण ऋषि बृहस्पति के पधारने पर न तो इन्द्र ही खड़े हुए और न ही अन्य देवों को खड़े होने दिया।देवगुरु बृहस्पति इन्द्र का ये कठोर दुर्व्यवहार देख कर चुप चाप वहां से लौट गए।कुछ देर पश्चात जब देवराज का मद उतरा तो उन्हे अपनी गलती का एहसास हुआ।