🎥 Top 5 Deep Learning Sessions at GTC
👁 1 раз ⏳ 83 сек.
NVIDIA’s GPU Technology Conference #GTC19 is the premier #AI conference, offering hundreds of workshops, sessions, and keynotes hosted by organizations like Google, Amazon, Facebook as well as rising startups. GTC showcases the latest breakthroughs in AI training and inference, industry-changing technologies, and successful implementations from research to production.https://www.nvidia.com/en-us/gtc/topics/deep-learning-and-ai/
https://nvda.ws/2EWevk5

🎥 Разбор задачи 1649 acmp.ru Машинное обучение
👁 3 раз ⏳ 3422 сек.
Теги:

О проекте "3.5 задачи в неделю": разбор олимпиадных задач по программированию каждые 2 дня в прямом эфире в 10 вечера по Москве. Более подробно http://goo.gl/qa142q

В проекте разобрано более 450 задач acmp.ru, общая длина видео разборов более 350 часов.

Список всех разборов, доступных участникам проекта, приведён в таблице https://goo.gl/WaMLu1 В седьмом столбце указаны теги - темы задач. Как стать участником проекта, написано в статье http://goo.gl/sUTIgo Участие бесплатно.

Ведущий проекта Меньш

​Launching TensorFlow Lite for Microcontrollers

https://petewarden.com/2019/03/07/launching-tensorflow-lite-for-microcontrollers/

#artificialintelligence #deeplearning #microcontrollers #tensorflow #tensorflow20

🔗 Launching TensorFlow Lite for Microcontrollers
I’ve been spending a lot of my time over the last year working on getting machine learning running on microcontrollers, and so it was great to finally start talking about it in public for the…

​AI & Architecture
An Experimental Perspective

By Stanislas Chaillou, Harvard Graduate School of Design:

https://towardsdatascience.com/ai-architecture-f9d78c6958e0

#artificialintelligence #architecture #design #deeplearning #technology

🔗 AI & Architecture
An Experimental Perspective

​Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure

http://www.aies-conference.com/wp-content/papers/main/AIES-19_paper_220.pdf

#artificialintelligence #deeplearning #machinelearning

🔗

​This Experiment Questions Some Recent AI Results

🔗 This Experiment Questions Some Recent AI Results
Audible - check out Nick Bostrom's "Superintelligence": US: https://amzn.to/2RXr32F EU: https://amzn.to/2SqauwI 📝 The paper "Approximating CNNs with Bag-of-l...

Запись трансляции ML тренировки 09.03.19 | Kaggle Elo, Whale, Tellus Satellite

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

https://www.youtube.com/watch?v=toGqk2wNz8k

🎥 Запись трансляции ML тренировки 09.03.19 | Kaggle Elo, Whale, Tellus Satellite
👁 3 раз ⏳ 8191 сек.
- Николай Сергиевский — Детектирование объектов на спутниковых снимках (The 2nd Tellus Satellite Challenge, xView: Objects in Context in Overhead Imagery)
- Юрий Болконский — Определение лояльности пользователей (Kaggle Elo Merchant Category Recommendation)
- Владислав Шахрай — Идентификация китов по изображениям (Kaggle Humpback Whale Identification)

Каждые две недели в Яндексе проходят тренировки по машинному обучению. Эти встречи помогают участникам конкурсов в сфере анализа данных пообщаться и обменять

​История второго места в Russian AI Cup 2018: CodeBall

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data
Я студент третьего курса, и в самом начале учёбы в университете я узнал про соревнования по искусственному интеллекту Russian Ai Cup, а позже и Mini Ai Cup, и начал в них активно участвовать, показывая неплохие результаты. В этот раз RAIC выпадал прямо на сессию, поэтому ничто не могло меня остановить :) И сегодня хочу рассказать вам, как мне удалось занять второе место.

Правила конкурса можно почитать на сайте соревнования, а также в этой статье. Ссылка на мой профиль: russianaicup.ru/profile/TonyK.

https://habr.com/ru/company/mailru/blog/440924/

🔗 История второго места в Russian AI Cup 2018: CodeBall
Всем привет! Я студент третьего курса, и в самом начале учёбы в университете я узнал про соревнования по искусственному интеллекту Russian Ai Cup, а позже и Mi...

​Web data scraping with Python by Brian Keegan University of Colorado, 2019.
Enjoy scraping web with detailed tutorial

https://github.com/CU-ITSS/Web-Data-Scraping-S2019

🔗 CU-ITSS/Web-Data-Scraping-S2019
Contribute to CU-ITSS/Web-Data-Scraping-S2019 development by creating an account on GitHub.

​12 марта в 20:00 (мск) приходите посмотреть и послушать открытый вебинар «Метрические алгоритмы классификации» Запишитесь, чтобы не забыть: https://otus.pw/98tb/

На этом занятии вас ждет знакомство с метрическими алгоритмами классификации, вы рассмотрите алгоритм kNN и влияние нормализации данных в kNN. Также обсудим практические примеры использования метрических алгоритмов классификации.

Проведет вебинар преподаватель онлайн-курса «Data Scientist» Александр Никитин, разработчик и data scientist с 5-летним опытом, Chief data scientist и сооснователь Poteha AI, основатель broca.tech.

Вебинар проводится в рамках набора на курс «Data Scientist». Познакомьтесь с программой и пройдите вступительный тест: https://otus.pw/3Kzb/

Подключайтесь – будет интересно и профессионально.

🔗 Data Scientist — обучение от профессионалов. Data Science learning, Data Scientist learning | OTUS
Большие объемы данных, как с ними работать? Мы проводим курсы по BigData в Москве и трудоустраиваем наших программистов

🎥 Lecture 8 part 2: Deep Neural Networks
👁 1 раз ⏳ 2762 сек.
This is Lecture 8 - part 2 - of the KT EP3260 Fundamentals of Machine Learning over Networks (MLoNs). This lecture reviews the fundamentals and recent advances of deep neural networks (DNNs). In particular, this lecture covers its non-convex optimization landscape, various algorithms to address it, back propagation, preconditioning the optimization landscape, adaptive step size (including ADAM, RMSprop, and ADGRAD), and batch normalization. It then addresses learning and inference over networks, where we ma

​Putting ML in production I: using Apache Kafka in Python.
Using a message broker to productionise algorithms in real time

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

https://towardsdatascience.com/putting-ml-in-production-i-using-apache-kafka-in-python-ce06b3a395c8?source=collection_home---4------1---------------------

🔗 Putting ML in production I: using Apache Kafka in Python.
Using a message broker to productionise algorithms in real time

​What to expect from Reinforcement Learning?
And what’s beyond Imitation Learning

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

https://towardsdatascience.com/what-to-expect-from-reinforcement-learning-a22e8c16f40c?source=topic_page---------2------------------1

🔗 What to expect from Reinforcement Learning?
And what’s beyond Imitation Learning

🎥 'How neural networks learn' - Part III: The learning dynamics behind generalization and overfitting
👁 1 раз ⏳ 1355 сек.
In this third episode on "How neural nets learn" I dive into a bunch of academical research that tries to explain why neural networks generalize as wel as they do. We first look at the remarkable capability of DNNs to simply memorize huge amounts of (random) data. We then see how this picture is more subtle when training on real data and finally dive into some beautiful analysis from the viewpoint on information theory.

Main papers discussed in this video:
First paper on Memorization in DNNs: https://arxiv

Linear Regression with TF Keras

https://www.youtube.com/watch?v=oGuCxVyEhiA

🎥 Linear Regression with TF Keras
👁 1 раз ⏳ 937 сек.
In this video we learn how to perform linear regression with Keras in TensorFlow.

Keras is TensorFlow's high level API for building deep learning models.

Email: [email protected]
Website: https://www.poincaregroup.com
LinkedIn: https://www.linkedin.com/in/carlos-lara-1055a16b/

​It’s Only Natural: An Excessively Deep Dive Into Natural Gradient Optimization
This post gives an intuitive explanation of an approach called Natural Gradient, an elegant way to dynamically adjust gradient step size.

https://towardsdatascience.com/its-only-natural-an-excessively-deep-dive-into-natural-gradient-optimization-75d464b89dbb?source=collection_home---4------0---------------------

🔗 It’s Only Natural: An Excessively Deep Dive Into Natural Gradient Optimization
This post gives an intuitive explanation of an approach called Natural Gradient, an elegant way to dynamically adjust gradient step size.

🎥 Week 4 CS294-158 Deep Unsupervised Learning (2/20/19)
👁 1 раз ⏳ 8989 сек.
UC Berkeley CS294-158 Deep Unsupervised Learning (Spring 2019)
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkeley-cs294-158-sp19/home
Week 4 Lecture Contents:
- Latent Variable Models (ctd)
- Bits-Back Coding

​MIT 6.050J Information and Entropy, Spring

🔗 MIT 6.050J Information and Entropy, Spring 2008 - YouTube
Instructors: Paul Penfield, Seth Lloyd This course explores the ultimate limits to communication and computation, with an emphasis on the physical nature of ...


🎥 Unit 1: Bits and Codes, Lecture 2 | MIT 6.050J Information and Entropy, Spring 2008
👁 2 раз ⏳ 6200 сек.
* Note: Due to technical difficulties, not all the lectures for this course are available.
Unit 1: Bits and Codes, Lecture 2
Instructors: Paul Penfield, Seth Lloyd
See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 2: Compression, Lecture 1 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 4769 сек.
Unit 2: Compression, Lecture 1
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 3: Noise and Errors, Lecture 2 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 7009 сек.
Unit 3: Noise and Errors, Lecture 2

Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 4: Probability, Lecture 1 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 6730 сек.
Unit 4: Probability, Lecture 1
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 4: Probability, Lecture 2 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 6605 сек.
Unit 4: Probability, Lecture 2
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 5: Communications, Lecture 1 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 6577 сек.
Unit 5: Communications, Lecture 1
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 5: Communications, Lecture 2 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 6459 сек.
Unit 5: Communications, Lecture 2
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🎥 Unit 6: Processes, Lecture 1 | MIT 6.050J Information and Entropy, Spring 2008
👁 1 раз ⏳ 6419 сек.
Unit 6: Processes, Lecture 1
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

​5 Tools To Evaluate Machine Learning Model Fairness and Bias.
Introducing Some Tools To Easily Evaluate and Audit Machine Learning Models For Fairness and Bias

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

https://medium.com/@will.badr/5-tools-to-evaluate-machine-learning-model-fairness-and-bias-66cb78920878?source=topic_page---------0------------------1

🔗 5 Tools To Evaluate Machine Learning Model Fairness and Bias.
Introducing Some Tools To Easily Evaluate and Audit Machine Learning Models For Fairness and Bias

​PCI Express vs. Thunderbolt - How much performance drop of your GPU you will have if you put it in eGPU

https://egpu.io/forums/mac-setup/pcie-slot-dgpu-vs-thunderbolt-3-egpu-internal-display-test/

🔗 PCI Express vs. Thunderbolt - How much performance drop of y...
This is the question that many users wants to know: How much performance drop of my Video Card i will have if i put it in the eGPU with Thunderbolt 1,...

​Knitting and Recommendations
How Computers Think: Part Three

https://towardsdatascience.com/knitting-and-recommendations-b9d178a86c97?source=collection_home---4------1---------------------

🔗 Knitting and Recommendations
How Computers Think: Part Three