🎥 TMPA School 2018 Saratov: Сетевые методы анализа многомерных данных (часть 1)
👁 1 раз ⏳ 5235 сек.
Ростислав Яворский
Доцент департамента анализа данных и искусственного интеллекта факультета компьютерных наук, НИУ ВШЭ

Смотреть презентацию: https://speakerdeck.com/exactpro/sietievyie-mietody-analiza-mnoghomiernykh-dannykh-chast-1

TMPA School 2018
Тестирование программного обеспечения, анализ данных и машинное обучение
https://school.tmpaconf.org/

🎥 Training Large-Scale Deep Nets with RL with Nando de Freitas - TWiML Talk #213
👁 1 раз ⏳ 3322 сек.
Today we close out both our NeurIPS series and our 2018 conference coverage with this interview with Nando de Freitas, Team Lead & Principal Scientist at Deepmind and Fellow at the Canadian Institute for Advanced Research.

In our conversation, we explore his interest in understanding the brain and working towards artificial general intelligence through techniques like meta-learning, few-shot learning and imitation learning. In particular, we dig into a couple of his team’s NeurIPS papers: “Playing hard

​Christmas Games: How random are dice?

https://towardsdatascience.com/christmas-games-how-random-are-dice-969f8a935b18?source=collection_home---4------1---------------------

🔗 Christmas Games: How random are dice? – Towards Data Science
Analyzing dice randomness with openCV and hypothesis testing.

как пройти Data Science собеседование
https://www.youtube.com/watch?v=OHhoLhYW2cg

🎥 5 Steps to Pass Data Science Interviews
👁 2 раз ⏳ 823 сек.
Data Science is becoming more and more popular as a career choice since it offers both lucrative salaries and the opportunity to have high impact. The Data Science interview process is challenging, but with dedicated practice you can succeed. In this video, I'll outline the 7 steps to pass any Data Science Interview. We'll go over topics like studying techniques, portfolio optimization, and interviewing tips, all of which are prominent in the modern Data Science interview pipeline. I've listed all of the re

​Новогодний датасет 2018: открытая семантика русского языка.

Открытая семантика русского языка получила большое обновление. Было собрано достаточное количество данных, чтобы применить поверх собранной разметки машинное обучение и построить семантическую модель языка. Смотрим, что из этого получилось: http://amp.gs/ES2r

🔗 Новогодний датасет 2018: открытая семантика русского языка
Открытая семантика русского языка, об истории создания которой вы можете прочитать здесь и здесь, получила большое обновление. Мы собрали достаточное количество...

​Deep Learning for Recommender Systems by Oliver Gindele

🔗 Deep Learning for Recommender Systems by Oliver Gindele
Recommender systems are widely used by e-commerce and services companies worldwide to provide the most relevant items to their users. Over the past few years...

​Music Composition using Deep Learning - Code in Python

🔗 Music Composition using Deep Learning - Code in Python
Deep Learning Oleh: J.COp #UNTUK_INDONESIA =================================================== This Video: Training a LSTM on Indonesian Folk Songs in MIDI f...

I Worked With A Data Scientist As A Software Engineer. Here’s My Experience.
https://towardsdatascience.com/i-worked-with-a-data-scientist-heres-what-i-learned-2e19c5f5204?source=collection_home---4------5---------------------

https://habr.com/post/434390/

Адекватность машинных переводчиков

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://yangx.top/ai_machinelearning_big_data

​Prevent NVIDIA GPUs' throttling on headless server

🔗 Prevent NVIDIA GPUs' throttling on headless server
Prevent NVIDIA GPUs' throttling on headless server - gpu-control.md

​https://habr.com/company/dins/blog/433166/

Предсказываем время решения тикета с помощью машинного обучения

🔗 Предсказываем время решения тикета с помощью машинного обучения
Оформляя тикет в системе управления проектами и отслеживания задач, каждый из нас рад видеть ориентировочные сроки решения по своему обращению. Получая поток...

​Text Classification with State of the Art NLP Library — Flair

https://towardsdatascience.com/text-classification-with-state-of-the-art-nlp-library-flair-b541d7add21f

🔗 Text Classification with State of the Art NLP Library — Flair
Exciting news! A new version of Flair - state-of-the-art NLP library has just been released. Learn how to use it for text classification

​Машинное Обучение. Лекции
https://www.youtube.com/playlist?list=PL0Ks75aof3Ti5IBKAD4234VkMVnI4ecky

🔗 Машинное Обучение. Лекции - YouTube



🎥 Машинное обучение. Лекция 3. Линейные модели
👁 293 раз ⏳ 2867 сек.
В данном видео речь идёт о линейных моделях в задачах регрессии и классификации. Введены такие понятия, как регуляризация, функция правдоподобия и градиентный спуск.

DeepXmas: AI knows if you are naughty or nice
https://towardsdatascience.com/deepxmas-ai-knows-if-you-are-naughty-or-nice-2bd00b2ad3d2?source=collection_home---4------5---------------------

https://habr.com/company/ods/blog/433586/

Как мы не выиграли хакатон

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://yangx.top/ai_machinelearning_big_data

​Релиз PyTorch 1.0 Stable, библиотеки для машинного обучения от Facebook
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Ссылка: https://tproger.ru/news/pytorch-1-0-release/

🔗 Релиз стабильной версии ML-библиотеки PyTorch 1.0
В PyTorch 1.0 добавили поддержку крупных облачных платформ, интерфейс на C++, набор JIT-компиляторов и различные улучшения.

​Deep learning cheatsheets, covering content of Stanford’s CS 230 class.

CNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
RNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
TipsAndTricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks

#cheatsheet #Stanford #dl #cnn #rnn #tipsntricks

🔗 CS 230 - Convolutional Neural Networks Cheatsheet
Teaching page of Shervine Amidi, Graduate Student at Stanford University.

​Who is a Data Scientist? | How to become a Data Scientist? | Data Science Course | Edureka

🔗 Who is a Data Scientist? | How to become a Data Scientist? | Data Science Course | Edureka
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Who is a Data Scientist" wi...

How to Learn Data Science: Staying Motivated.
https://towardsdatascience.com/how-to-learn-data-science-staying-motivated-8665ed649687?source=collection_home---4------0---------------------

​How to Reduce Variance in the Final Deep Learning Model With a Horizontal Voting Ensemble

https://machinelearningmastery.com/horizontal-voting-ensemble/

🔗 How to Reduce Variance in the Final Deep Learning Model With a Horizontal Voting Ensemble
Predictive modeling problems where the training dataset is small relative to the number of unlabeled examples are challenging. Neural networks can perform well on these types of problems, although they can suffer from high variance in model performance as measured on a training or hold-out validation datasets. This makes choosing which model to use as …

​🔗 DL_17: Handling Color Image in Neural Network aka Stacked Auto Encoders (Denoising)
This lecture will discuss how to handle (feed) color images into stacked Auto Encoders.