​ResNeXt models pre-trained on Instagram hashtags stand out in their
in their ability to generalized to the 'ImageNetV2' test set
#PyTorch

https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/GeneralizationToImageNetV2.ipynb/

🔗 Google Colaboratory

🎥 Beyond the Hype. Real Companies Doing Real Business with AI - Alyssa Rochwerger | ODSC West 2018
👁 1 раз ⏳ 1796 сек.
AI - everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI.
This presentation provides:
Showcase companies getting actual real value from leveraging artificial intelligence and discuss ideas around how any company, from SMB to enterprise, can use artificial intelligence within their own bu

​Predicting the Generalization Gap in Deep Neural Networks

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Predicting the Generalization Gap in Deep Neural Networks
Posted by Yiding Jiang, Google AI Resident Deep neural networks (DNN) are the cornerstone of recent progress in machine learning, and ...

🎥 Teaching a Machine to Code
👁 1 раз ⏳ 2565 сек.
At Prodo.AI, we’re teaching machines to write code for humans. Using the latest in Deep Learning techniques, we can generate code that’s not just functional, but beautiful. Our goal is to make the computer do the heavy lifting so you can concentrate on the important things: being creative, solving problems, and having fun.We’ve tried a hundred different ways of encoding the knowledge of how to write code. In this talk, Samir will take you through a tour of the different techniques, architectures and optimis

​Keras Callbacks Explained

🔗 Keras Callbacks Explained
A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.

​Web Scraping news articles in Python

🔗 Web Scraping news articles in Python
Building a web scraping application in Python made simple

​Knowledge Quadrant for Machine Learning - Towards Data Science

🔗 Knowledge Quadrant for Machine Learning - Towards Data Science
Transfer Learning, Uncertainty Sampling, and Diversity Sampling to improve your Machine Learning models.

​Online poker — When’s the Money?

🔗 Online poker — When’s the Money?
The best time to play and how much it’s worth

​10 фич для ускорения анализа данных в Python
https://habr.com/ru/post/457302/

🔗 10 фич для ускорения анализа данных в Python
Источник Советы и рекомендации, особенно в программировании, могут быть очень полезны. Маленький шоткат, аддон или хак может сэкономить кучу времени и серьёзно у...

​BTGym
https://github.com/notadamking/RLTrader/

Scalable event-driven RL-friendly backtesting library. Build on top of Backtrader with OpenAI Gym environment API.
Backtrader is open-source algorithmic trading library:
GitHub: http://github.com/mementum/backtrader
Documentation and community:
http://www.backtrader.com/
OpenAI Gym is..., well, everyone knows Gym:
GitHub: http://github.com/openai/gym
Documentation and community:
https://gym.openai.com/

🔗 notadamking/RLTrader
A profitable cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym - notadamking/RLTrader

​Kaggle: Google AI Open Images – Константин Гаврильчик

🔗 Kaggle: Google AI Open Images – Константин Гаврильчик
Константин Гаврильчик рассказывает про соревнования Kaggle: Google AI Open Images, задача которых заключалась в детектировании объектов и связей между ними на изображениях. Решение Константина принесло серебряную медаль. Из видео вы сможете узнать: - Какая метрика использовалась - Особенности разметки и датасета - Подходы к решению и наилучшие решения Узнать о текущих соревнованиях можно на сайте http://mltrainings.ru/ Узнать о новых тренировках и видео можно из групп: ВКонтакте https://vk.com/mltrainin

​Sarcasm Detection: Step towards Sentiment Analysis - DIGVIJAY SINGH - Medium

🔗 Sarcasm Detection: Step towards Sentiment Analysis - DIGVIJAY SINGH - Medium
Humans have a social nature. Social nature means that we interact with each other in positive, friendly ways, and it also means that we…

​Sean Carroll: The Nature of the Universe, Life, and Intelligence | Artificial Intelligence Podcast

🔗 Sean Carroll: The Nature of the Universe, Life, and Intelligence | Artificial Intelligence Podcast
Sean Carroll is a theoretical physicist at Caltech, specializing in quantum mechanics, gravity, and cosmology. He is the author of several popular books: one on the arrow of time called From Eternity to Here, one on the Higgs boson called The Particle at the End of the Universe, and one on science and philosophy called The Big Picture: On the Origins of Life, Meaning, and the Universe Itself. He has an upcoming book on Quantum Mechanics that you can preorder now called Something Deeply Hidden. He writes one

Deep Learning

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
AI и естественный язык
Краткая история криптографии
Курс по криптографии. Вводное занятие
Курс по криптографии. Симметричные шифры

#video

🎥 Дмитрий Коробченко: Deep Learning
👁 2 раз ⏳ 6062 сек.
Руководитель проектов и инженер в области машинного обучения, компьютерного зрения и обработки сигналов в Исследовательском Центре Samsung Дмитрий ...

🎥 Сергей Марков: AI и естественный язык
👁 1 раз ⏳ 7969 сек.
1 июня 2016 в антикафе «Кочерга» (http://kocherga-club.ru/) автор одной из сильнейших российских
шахматных программ, специалист по методам машинног...

🎥 Сергей Владимиров: Краткая история криптографии
👁 1 раз ⏳ 8259 сек.
8 сентября 2016 Сергей Владимиров рассказал в Кочерге (http://kocherga-club.ru ) об истории криптографии.

Сергей Владимиров (https://vk.com/vlser...

🎥 Дмитрий Яхонтов: Курс по криптографии. Вводное занятие
👁 1 раз ⏳ 3940 сек.
Курс расскажет об основных понятиях современной криптографии. Будут рассмотрены протоколы шифрования, проверки подлинности, обмена ключами, а также...

🎥 Дмитрий Яхонтов: Курс по криптографии. Симметричные шифры
👁 1 раз ⏳ 4271 сек.
Тема лекции - симметричные шифры:
- Основные принципы построения криптосистем.
- Протоколы, использующие один и тот же ключ для шифрования и для р...

​Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning. arxiv.org/abs/1907.03029

🔗 Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning
Blind and universal image denoising consists of a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for Gaussian noise. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network's generalization strength to unseen noise levels. We also adapt the fusion denoising network architecture for real image denoising. Our approach improves real-world grayscale image denoising PSNR results by up to $0.7dB$ for training noise levels and by up to $2.82dB$ on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of $0.1dB$, whether trained on or not.

🎥 Python Seaborn Tutorial | Data Visualization in Python Using Seaborn | Edureka
👁 1 раз ⏳ 1241 сек.
** Python Certification Training: https://www.edureka.co/python **
This Edureka video on 'Python Seaborn Tutorial' is to educate you about data visualizations using Seaborn in Python. Below are the topics covered in this video:

Introduction to Seaborn
Seaborn vs Matplotlib
How to install Seaborn
Installing dependencies
Seaborn Plotting functions
Multi-plot grids
Plot-Aesthetics

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

#Edureka #PythonEdureka #PythonSeabornTut

​Growing your own RNN cell : Simplified - Towards Data Science

🔗 Growing your own RNN cell : Simplified - Towards Data Science
Take a peek into the ‘deep’ world of a single RNN cell

​Extreme Rare Event Classification: A Straight Forward Solution

🔗 Extreme Rare Event Classification: A Straight Forward Solution
In this article we will approach rare events detection on a real world dataset (web break on a paper mill) using machine learning…

​Reliving and telling my backpacking adventures with data, part 3

🔗 Reliving and telling my backpacking adventures with data, part 3
Interpreting my experiences with data from Fitbit, Spotify, and sensors

🎥 Bespoke Machine Learning Processor Development Framework on Flexible Substrates
👁 1 раз ⏳ 1544 сек.
Title:
Bespoke Machine Learning Processor Development Framework on Flexible Substrates

Abstract:
This paper proposes a framework for the design of bespoke machine learning (ML) processors on flexible substrates (e.g. plastic) to address an important need in flexible and wearable applications – a processing engine of the flexible electronics applications. The proposed framework automates the design of bespoke ML processors on flexible substrates to reduce development time, and therefore the time-to-market.