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Andriy Burkov

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Q&A

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Jeremy Picart

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Yoan Hyvernaud Nayil Kalla J’ai l’impression d’assister à un autre débat sans fin en lien avec la chocolatine

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Anamika T.

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As long as you are a programmer / developer you can do both . The concepts does not change . If you can code in Python then even R is possible ! It is not rocket science !

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Arindam Dutta

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R is like the Oxygen and Python is like the Nitrogen in terms of number of users perspective.. Although R contains "20.6%" in the atmosphere but still you can't live without it in the data science world ..but it is an unfair comparative study to use Python just bcz no of users are more.. Statisticians prefer R but CS people prefers Python.. But no of statisticians are much lesser than the engineers obviously. So it can't be a judgemental criteria.

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David McNeil

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This debate is not necessary & is going too far. My advice would be learn both if you can as it would help maybe not now but sometimes in the future.

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Conrad Thiele

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lol :D :D :D unless it referenced in a pirates joke....RRRrrrr

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Matt Risley

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"you can safely ignore R" until you land a job in a SAS shop.... whatever this message is intended to be, it is painfully ignorant of the state of analytics, which is and always will be software agnostic. it is also runs counter to the ethics of the open source framework, which promotes diversity of thought and methods. lastly, this is poor career advice, for too many reasons to list here.

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Anh Hoang Duc

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We use R and Pythin in my bank. R is much better than Python when you want to get insights but I admit that ML in Python is still better. You can not ignore R if your job is to focus on getting insights of business

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Alexander J. Orona

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Python. There might be unusual circumstances where you need R, but there are usually work arounds. If you're interested in deep learning, I don't know if there are any good R options. For DL, I use (and everyone I know uses) either Tensorflow or PySpark. Here's an overview of 28K data practitioners commissioned by Kaggle last year: https://rpubs.com/cvrajesh/kagglesurvey2018

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Neha Sharma

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That would be good bye to using shiny framework and hello to tkinter

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Mark Kittisopikul, Ph.D.

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https://julialang.org/ ?

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Andriy Burkov

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This week's issue of my AI newsletter was emailed to the subscribers. In this issue: on using natural language in robotics; an 8-bit inference method that saves 2x memory and does not degrade performance for 175B models; a library for representing Python programs as graphs for machine learning; are designers even needed anymore?; tutorials on accelerating BERT inference and on supervised clustering; GPT language model spells out new proteins; and more. #artificialintelligence #ai #machinelearning #ml #deeplearning #analytics

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This week's issue of my AI newsletter has just been emailed to the subscribers! In this issue: on using natural language in robotics; an 8-bit inference method that saves 2x memory and does not degrade performance for 175B models; a library for representing Python programs as graphs for machine learning; are designers even needed anymore?; tutorials on accelerating BERT inference and on supervised clustering; GPT language model spells out new proteins; and more. Subscribe to receive future issues in your email inbox. #artificialintelligence #ai #machinelearning #ml #deeplearning #analytics

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This week's issue of my AI newsletter was emailed to the subscribers. In this issue: deep dive into Neural Radiance Fields; EvoTorch: an advanced evolutionary computation library; automated techniques could make it easier to develop AI; how AI image generators like Craiyon see our world; new protonic programmable resistors may help speed learning in deep neural networks; introduction to streaming for data scientists; and more. Subscribe to receive future issues in your email inbox. #artificialintelligence #ai #machinelearning #ml #deeplearning #datascience #analytics

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Andriy Burkov

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This week's issue of my AI newsletter has just been emailed to the subscribers. In this issue: deep dive into Neural Radiance Fields; EvoTorch: an advanced evolutionary computation library; automated techniques could make it easier to develop AI; how AI image generators like Craiyon see our world; new protonic programmable resistors may help speed learning in deep neural networks; introduction to streaming for data scientists; and more. Subscribe to receive future issues in your email inbox. #artificialintelligence #ai #machinelearning #ml #deeplearning #datascience #analytics

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This week's issue of my AI newsletter was emailed to the subscribers. In this issue: can AI really help us talk to animals?; AlphaFold reveals the structure of the protein universe; highly accurate dichotomous image segmentation; why AI and machine learning are drifting away from the cloud; AI-generated cinematic posters; tutorial on mixed precision training; and more. Subscribe to receive future issues in your email inbox. #artificialintelligence #ai #machinelearning #ml #deeplearning #analytics

Artificial Intelligence #131

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This week's issue of my AI newsletter has been emailed to the subscribers. In this issue: a short chronology of deep learning for tabular data; robot performs diverse household tasks via exploration after watching one human video; ML-enhanced code completion improves developer productivity; a course on geometric deep learning; masked siamese networks for label-efficient learning; and more. Subscribe to receive future issues in your email inbox. #artificialintelligence #ai #machinelearning #ml #deeplearning #analytics

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This week's issue of my AI newsletter has been emailed to the subscribers. In this issue: one model for four tasks of object tracking, a pruned BERT-Large faster than DistilBERT while retaining the accuracy; why do tree-based models still outperform deep learning on tabular data?; will AI steal submarine's stealth?; how artificial intelligence is changing astronomy; on privacy-preserving machine learning; and more. Subscribe to receive future issues in your email inbox. #artificialintelligence #ai #machinelearning #ml #deeplearning #analytics