🎥 This is How Google’s Phone Enhances Your Photos
👁 1 раз ⏳ 278 сек.
📝 The paper "Handheld Multi-frame Super-resolution" is available here:
https://sites.google.com/view/handheld-super-res/

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🎥 Easy Face Recognition Tutorial With JavaScript
👁 1 раз ⏳ 1312 сек.
In this video we will be setting up face recognition for any image using AI. This AI is able to recognize the name of every character in an image very quickly without much performance overhead. We will be using the Face API JS library built on Tensor Flow to setup the face recognition.

By the end of this video you will have fully functional face recognition on your site which can be used with any image. It is even easily extensible to recognize any other person by simply adding a picture of their face and

​Free Will, Clairvoyant Demons, and Determinism

🔗 Free Will, Clairvoyant Demons, and Determinism
Determinism, generative machine learning, and whether or not free will in humans (or machines) is possible

​https://towardsdatascience.com/moving-from-keras-to-pytorch-f0d4fff4ce79?source=collection_home---4------1-----------------------/

🔗 Moving from Keras to Pytorch
Why? How? It's not that difficult.

🎥 Big Data n°6: Intro to machine learning with Big Data by Chris Evers
👁 1 раз ⏳ 9959 сек.

🎥 3 Limits of Artificial Intelligence
👁 6 раз ⏳ 862 сек.
AI has enabled so many new opportunities for people to create a positive impact in the world by creating engineering solutions across every industry! However, AI is still evolving and we have to address its limitations as well. In this video, I'll explain 3 major limits of AI - a lack of causal reasoning, vulnerability to adversarial examples, and a lack of interpretability. I'll also explain ways to solve these limits and earn a profit doing so. The next time someone asks you what AI can't currently do, sh

​Logic Gates Part 2

🔗 Logic Gates Part 2
A guide to understanding basic logic gates

​Рекомендательные системы: идеи, подходы, задачи

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Многие привыкли ставить оценку фильму на КиноПоиске или imdb после просмотра, а разделы «С этим товаром также покупали» и «Популярные товары» есть в любом интернет- магазине. Но существуют и менее привычные виды рекомендаций. В этой статье я расскажу о том, какие задачи решают рекомендательные системы, куда бежать и что гуглить.
https://habr.com/ru/company/jetinfosystems/blog/453792/

🔗 Рекомендательные системы: идеи, подходы, задачи
Многие привыкли ставить оценку фильму на КиноПоиске или imdb после просмотра, а разделы «С этим товаром также покупали» и «Популярные товары» есть в любом инте...

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://towardsdatascience.com/basic-principles-to-create-a-time-series-forecast-6ae002d177a4?source=collection_home---4------0-----------------------

🔗 Basic Principles to Create a Time Series Forecast
Explaining the basics steps to create time series forecasts.

​Mingxing Tan and Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019. Arxiv link: https://arxiv.org/abs/1905.11946.
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

🔗 tensorflow/tpu
Reference models and tools for Cloud TPUs. Contribute to tensorflow/tpu development by creating an account on GitHub.

🎥 Deep Learning на пальцах 15 - Заключение
👁 7 раз ⏳ 5641 сек.
Курс: http://dlcourse.ai

​When and when not to A/B test

🔗 When and when not to A/B test
Split test vs. multi-armed bandit: simulation, source code and ready-to-use app

🎥 Азамат Бердышев - Элегантные абстракции в вычислениях машинного обучения
👁 1 раз ⏳ 3967 сек.
VI DS/ML Meetup Astana

4) Абстракции в вычислениях машинного обучения.
Азамат Бердышев рассмотрит конфликт между dynamism, generics & speed в вычислениях машинного обучения. Он попытается донести, что при правильной абстракции вычислений, несмотря на то, что мы наблюдаем в Python, R, MATLAB и др., можно писать код, обладающий всеми тремя качествами.

Мы также попытаемся понять, какие из существующих ограничений софта являются фундаментальными (т.е. от железа), а какие случайными (т.е. связаны с дизайном с

​50,000 training samples

https://www.profillic.com/paper/arxiv:1905.10498

🔗 Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language processing, robotics, computer vision, data mining, neural networks, artificial intelligence/AI, data science... and explore working together on projects, github code

🎥 Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Reinforcement Learning
👁 1 раз ⏳ 4727 сек.
Professor Emma Brunskill, Stanford University
http://onlinehub.stanford.edu/

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs234/index.html

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

To view a

🎥 How Dangerous are AI and Algorithms? | Martin Ford | Rubin Report
👁 1 раз ⏳ 2072 сек.
In this episode of The Rubin Report Dave Rubin talks to Martin Ford (Author and Futurist) about AI, the power of computers and robots, Deep Learning, his views on promoting Universal Basic Income, and more. **Support The Rubin Report: http://www.rubinreport.com/donate

Stay tuned for Part 2 of Dave's interview with Martin Ford coming tomorrow and the full interview airing Friday 5/31.

Subscribe to The Rubin Report: http://www.youtube.com/subscription_center?add_user=RubinReport

See Dave LIVE: https://da

🎥 Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Carlo Tree Search
👁 1 раз ⏳ 4031 сек.
Professor Emma Brunskill, Stanford University
http://onlinehub.stanford.edu/

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs234/index.html

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

To view a

​EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling

http://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html

🔗 EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
Posted by Mingxing Tan, Staff Software Engineer and Quoc V. Le, Principal Scientist, Google AI Convolutional neural networks (CNNs) are c...

🎥 Should AI Research Try to Model the Human Brain?
👁 1 раз ⏳ 420 сек.
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📝 The paper "Reinforcement Learning, Fast and Slow" is available here:
https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30

​Соревнование ML-систем на лингвистическом материале. Как мы учились заполнять пропуски

https://habr.com/ru/company/abbyy/blog/453974/

🔗 Соревнование ML-систем на лингвистическом материале. Как мы учились заполнять пропуски
Каждый год в Москве проходит конференция "Диалог", в которой участвуют лингвисты и специалисты по анализу данных. Они обсуждают, что такое естественный язык, как...