​Google opensourced their lib GPipe for training really big networks on many machines

https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html?m=1

🔗 Introducing GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models
Posted by Yanping Huang, Software Engineer, Google AI Deep neural networks (DNNs) have advanced many machine learning tasks, including ...

​🔗 OpenAI launches Neural MMO, a massive reinforcement learning simulator
Artificial intelligence that’s beastly at World of Warcraft might not lie too far into the distant future, if OpenAI has its way. The San Francisco research nonprofit today released Neural MMO, a “massively multiagent” virtual training ground that plops agents in the middle of an RPG-like world — one complete with a resource collection mechanic and player versus player combat. “The game genre of M

​Python Tutorial: Fuzzy Name Matching Algorithms

🔗 Python Tutorial: Fuzzy Name Matching Algorithms – Towards Data Science

🎥 Deep Genomics: Artificial Intelligence Meets The Human Genome
👁 1 раз ⏳ 5241 сек.
https://vlab.org/events/deep-genomics/

June 20, 2017, 6:00 p.m. at SRI International

====Moderator
Raeka Aiyar, Director of Scientific Strategy and Communications, Stanford Genome Technology Center

===Panelists
Charlene Son Rigby, SVP Customer Operations, Fabric Genomics
Helmy Eltoukhy, CEO, Guardant Health
Andy Felton, VP Marketing & Product Management, Ion Torrent Business
Ursheet Parikh, Partner, Mayfield Fund

===Event Team
Co-Chairs: Patricia Yotnda and Mike Chen
Event Team Members: Paul Dibyadeep,

🎥 Deep Learning course 2019, seminar #2
👁 13 раз ⏳ 1287 сек.
Семинар №2 курса https://dlcourse.ai/
Задание 1: https://github.com/sim0nsays/dlcourse_ai
Ноутбук 1, к которому мы ненадолго возвращаемся: https://colab.research.google.com/drive/1FBdo0TAv5eiWNl909vrcAQeau476rlOK

​Neural MMO — A Massively Multiagent Game Environment

By OpenAI: https://blog.openai.com/neural-mmo/

- Code: https://github.com/openai/neural-mmo
- 3D Client: https://github.com/jsuarez5341/neural-mmo-client

#artificialintelligence #deeplearning #multiagent #reinforcementlearning

🔗 Neural MMO - A Massively Multiagent Game Environment
We’re releasing our Neural MMO - a massively multiagent game environment for reinforcement learning agents.

Machine Learning with No Code

https://www.youtube.com/watch?v=w45t3itM5NM

🎥 Machine Learning with No Code
👁 1 раз ⏳ 704 сек.
Is it possible to use machine learning without needing to code? The answer is yes! Uber's AI lab recently open sourced python library called Ludwig that they've been using internally for 2 years. The tagline is that it allows anyone to use deep learning without coding. It will require some configuration and unix commands to setup, but I'll show you how in this video. I'll also talk about other code-free tools like Azure ML Studio, DataRobot, and DeepCognition. Enjoy!

Code for this video:
https://github.c

​A Gold-Winning Solution Review of Kaggle Humpback Whale Identification Challenge

https://towardsdatascience.com/a-gold-winning-solution-review-of-kaggle-humpback-whale-identification-challenge-53b0e3ba1e84?source=collection_home---4------0---------------------

🔗 A Gold-Winning Solution Review of Kaggle Humpback Whale Identification Challenge
An extensive yet simple review of the most noticeable approaches

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​Андрей Устюжанин: «Поиск темной материи в лабораторных условиях с помощью машинного обучения»

🔗 Андрей Устюжанин: «Поиск темной материи в лабораторных условиях с помощью машинного обучения»
21 февраля в Музее космонавтики состоялась вторая лекция цикла «Космос и технологии» на тему «Поиск темной материи в лабораторных условиях с помощью машинног...

Build a deep neural network in 4 mins with TensorFlow in Colab
https://www.youtube.com/watch?v=_VTtrSDHPwU

🎥 Build a deep neural network in 4 mins with TensorFlow on Colabs
👁 1 раз ⏳ 234 сек.
Google Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the Cloud. In this episode of Coding TensorFlow, Laurence shows us how to code, test, and train neural networks right in your browser, without having to worry about installing any kind of runtime. Watch to quickly see an example of how you can use TensorFlow to build a neural network for breast cancer classification...all this happens within Colab!



Download the pre-processed data & Colab to try it your

Tensor flow. Smooth dive

https://www.youtube.com/watch?v=cs9dBQxe8yM

🎥 Meetup #17. Tensor flow. Smooth dive
👁 8 раз ⏳ 3742 сек.
Видео доклада с семнадцатой встречи сообщества .Nuts. C докладом выступил Сергей Лозовской. Доклад состоялся 28 февраля 2019 года.

​Machine Learning
A Probabilistic Perspective
Kevin P. Murphy

📝 Machine-Learning-A-Probabilistic-Perspective.pdf - 💾26 293 504

🎥 Introduction to Deep Learning Lecture 14
👁 1 раз ⏳ 4422 сек.

Similarity learning using deep neural networks - Jacek Komorowski

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

https://www.youtube.com/watch?v=OkcS4qE4Zsg

🎥 Similarity learning using deep neural networks - Jacek Komorowski
👁 1 раз ⏳ 2116 сек.
PyData Warsaw 2018

Deep neural network give very good results in visual object recognition tasks, but they require large number of training examples from each category. I'll present a class of neural network architectures, that can be used when only few training examples from each class are available. They are based on 'similarity learning' concept and can be used to solve various practical problems.
===
www.pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the

​3 Levels of Deep Learning Competence

https://machinelearningmastery.com/deep-learning-competence/

🔗 3 Levels of Deep Learning Competence
Deep learning is not a magic bullet, but the techniques have shown to be highly effective in a large number of very challenging problem domains. This means that there is a ton of demand by businesses for effective deep learning practitioners. The problem is, how can the average business differentiate between good and bad practitioners? …

​Tutorial wineural differentiation
https://nbviewer.jupyter.org/github/urtrial/neural_ode/blob/master/Neural%20ODEs.ipynb

🔗 Notebook on nbviewer
Check out this Jupyter notebook!

​Learning Theory: Empirical Risk Minimization

https://towardsdatascience.com/learning-theory-empirical-risk-minimization-d3573f90ff77?source=collection_home---4------1---------------------

🔗 Learning Theory: Empirical Risk Minimization – Towards Data Science
Delving into ERM, one of the essential neglected concepts in machine learning

Practical Deep Learning For Coders 2018
https://www.youtube.com/watch?v=IPBSB1HLNLo&list=PLfYUBJiXbdtS2UQRzyrxmyVHoGW0gmLSM&index=2&t=0s

https://www.youtube.com/playlist?list=PLfYUBJiXbdtS2UQRzyrxmyVHoGW0gmLSM

🎥 Lesson 1: Deep Learning 2018
👁 133 раз ⏳ 5195 сек.
NB: Please go to http://course.fast.ai to view this video since there is important updated information there. If you have questions, use the forums at http://forums.fast.ai

Welcome to the start of your fast.ai journey! In today’s lesson you’ll set up your deep learning server, and training your first image classification model (a convolutional neural network, or CNN), which will learn to distinguish dogs from cats nearly perfectly. If you need help at any time, head over to forums.fast.ai where over a thou

🎥 Artificial Intelligence And Machine Learning E-Degree : Sneak Peak | Eduonix
👁 2 раз ⏳ 2819 сек.
Machine Learning is one of the technologies which gained immense popularity recently. This video gives a sneak peek of our AI and ML E-Degree campaign on Kickstarter. Herein, the tutor explains the different models of machine learning such as supervised, unsupervised and reinforcement ML models along with the insights into data science workflows and deep learning.

If you are one of those who is always intrigued by the concepts of AI and ML, then you can learn it from the very beginning by backing our AI a