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​How to build a Recommendation Engine quick and simple

🔗 How to build a Recommendation Engine quick and simple
Part 1: an introduction, how to get to production in a week and where to go after that

​The Complete Beginner’s Guide to Machine Learning: Multiple Linear Regression in 4 Lines of Code!

🔗 The Complete Beginner’s Guide to Machine Learning: Multiple Linear Regression in 4 Lines of Code!
Conquer the basics of multiple linear regression (and backward elimination!) and use your data to predict the future!

​Take Your Best Selfie Automatically, with Photobooth on Pixel 3
http://ai.googleblog.com/2019/04/take-your-best-selfie-automatically.html

🔗 Take Your Best Selfie Automatically, with Photobooth on Pixel 3
Posted by Navid Shiee, Senior Software Engineer and Aseem Agarwala, Staff Research Scientist, Google AI Taking a good group selfie can b...

​Chatbots aren’t as difficult to make as You Think

🔗 Chatbots aren’t as difficult to make as You Think
A Darth Vader Guide to building Chatbots

​Explaining probability plots

🔗 Explaining probability plots
In this article I would like to explain the concept of probability plots — what they are, how to implement them in Python and how to…

​Data Science Digest (April 2019)

🔗 Data Science Digest (April 2019)
Хабр, привет! В марте я восстановил публикацию на Хабре дайджеста посвященного ML и Data Science. Сегодня я подготовил свежую подборку интересных ссылок, а т...

​SNA Hackathon 2019 — итоги

🔗 SNA Hackathon 2019 — итоги
1-го апреля завершился финал SNA Hackathon 2019, участники которого соревновались в сортировке ленты социальной сети с использованием современных технологий маш...

​https://arxiv.org/pdf/1904.05440.pdf

🔗

​Play with #GAN(Generative Adversarial Networks) in your browser and better understand what's going on inside network
https://poloclub.github.io/ganlab/?fbclid=IwAR1xl1kmA4DflkXShjbufAD4EUOW6O9TxFcoBPI-DWHClIR4UhSD566d4XY

🔗 GAN Lab: Play with Generative Adversarial Networks in Your Browser!

​The Third Wave Data Scientist

🔗 The Third Wave Data Scientist
An update of the data science skill portfolio

🎥 Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI
👁 1 раз ⏳ 3363 сек.
Professor Christopher Manning, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.s

🎥 Байесовские методы в машинном обучении 5
👁 1 раз ⏳ 4723 сек.
Ветров Д.П.

​#NaturalLanguageProcessing,

Попробуем сформировать описание процесса появления новой звуковой единицы в естественном языке.

Для этого рассмотрим отдельно живущее племя. И построим цепочку ситуаций, которую необходимо совместно пережить нескольким особям этого племени, приводящую к формированию в языке нового звука.

Звук, который мы попробуем сформировать, будет тождественен фразе: "Видел мамонта".
https://habr.com/ru/post/446834/

🔗 Как в языке сформировать существительное? Сигнал («Видел мамонта»)
Попробуем сформировать описание процесса появления новой звуковой единицы в естественном языке. Для этого рассмотрим отдельно живущее племя. И построим цепочку с...

​Research Awards

🔗 Research Awards
From time to time we invite academics to propose research in specific areas that align with our mission of making the world more open and connected.

​What the Hell is a Neural Network? A 5 Minute Primer for Non-Engineers.

🔗 What the Hell is a Neural Network? A 5 Minute Primer for Non-Engineers.
Odds are that you’ve heard someone talk about “Artificial Intelligence” and “neural networks” in the recent past.

​http://arxiv.org/abs/1904.07290

🔗 Brain Tumor Segmentation on MRI with Missing Modalities
Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis. However, rather than having complete four modalities as in BraTS dataset, it is common to have missing modalities in clinical scenarios. We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path. We use self-supervised training through channel dropout and also propose a novel domain adaptation method on feature maps to recover the information from the missing channel. Our results demonstrate that the quality of the segmentation depends on which modality is missing. Furthermore, we also discuss and visualize the contribution of each modality to the segmentation results. Their contributions are along well with the expert screening routine.

​How to Automate Tasks on GitHub With Machine Learning for Fun and Profit

🔗 How to Automate Tasks on GitHub With Machine Learning for Fun and Profit
A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

​Which Democratic Candidate Gets the Most News Coverage?

🔗 Which Democratic Candidate Gets the Most News Coverage?
A Data Analysis of the 2020 Presidential Contenders

​Becoming a Data Scientist — When Dan Becker pointed out I had it all wrong

🔗 Becoming a Data Scientist — When Dan Becker pointed out I had it all wrong
After Dan Becker’s “Why Data Science Skill-Building Might Be Holding You Back (And What You Should Be Doing Instead).”…