🎥 Deep Learning for Anime & Manga, by Vie Jill-Jênn [INRIA & Mangaki]
👁 3 раз ⏳ 1379 сек.

​On the Relationship between Self-Attention and Convolutional Layers

https://github.com/epfml/attention-cnn

Paper: https://arxiv.org/abs/1911.03584v2

🔗 epfml/attention-cnn
Source code for "On the Relationship between Self-Attention and Convolutional Layers" - epfml/attention-cnn

​Mapping the tech world with t-SNE

🔗 Mapping the tech world with t-SNE
We analyse 200k tech news articles with the t-SNE algorithm

​Physical Theory of Intelligence

🔗 Physical Theory of Intelligence
v1.0

​An extensible Evolutionary Algorithm Example in Python

🔗 An extensible Evolutionary Algorithm Example in Python
Learning how to write an easy Evolutionary Algorithm from scratch in less than 50 lines of code that you can use for your projects.

🎥 DeepPhish: Simulating Malicious AI
👁 1 раз ⏳ 3054 сек.
91% of cybercrimes and attacks start with a phishing email. This means that cyber security researchers must focus on detecting phishing in all of its settings and uses. However, they face many challenges as they go up against sophisticated and intelligent attackers. As a result, they must use cutting-edge Machine Learning and Artificial Intelligence techniques to combat existing and emerging criminal tactics.

By Alejandro Correa Bahnsen

Full Abstract & Presentation Materials: https://www.blackhat.com/eu-1

​PyTorch 1.4 release

https://github.com/pytorch/pytorch/releases/tag/v1.4.0

- PyTorch Mobile - Build level customization
- Distributed Model Parallel Training (RPC)
- Java bindings
- End of python 2 support =)
- Pruning out-of-the box
- Learning rate schedulers (torch.optim.lr_scheduler) now support “chaining.”
- Named Tensors (out of beta?)
- AMD Support (!?)
- Quantization (!) - more modules support

🔗 pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch

​Deep Image Compression using Decoder Side Information

Code: https://github.com/ayziksha/DSIN

Paper: https://arxiv.org/abs/2001.04753v1

🔗 ayziksha/DSIN
Deep Image Compression using Decoder Side Information - ayziksha/DSIN

​Глубокое обучение / Deep Learning

1. Слои глубоких сверточных сетей
2. Alex (net)
3. VGG
4. GoogleNet
5. ResNet, InceptionResNet, DenseNet
6. SqueezeNet

#video #neural

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Открыть в Telegram



🎥 Untitled
👁 1 раз ⏳ 1061 сек.


🎥 Untitled
👁 1 раз ⏳ 330 сек.


🎥 Untitled
👁 1 раз ⏳ 198 сек.


🎥 Untitled
👁 1 раз ⏳ 275 сек.


🎥 Untitled
👁 1 раз ⏳ 320 сек.


🎥 Untitled
👁 1 раз ⏳ 129 сек.

​#MoscowTravelHack #DataScience #ПредиктивнаяАналитика

Задачи для DataSience-команд и аналитиков в рамках хакатона Moscow Travel Hack. Создай предиктивную модель покупки билета на основе данных о пользователе в сервисе «Мегафон Путешествия» или придумай рекомендательный сервис, который сможет построить индивидуальную программу посещения Москвы для иностранного туриста на платформе Russpass.

Эти и другие 10 задач можно найти на сайте хакатона travelhack.moscow
Призовой фонд — 1,1 млн рублей!

🔗 Moscow Travel Hack
Создай новые технологии и digital-решения для туризма. Призовой фонд 1.1 миллион рублей. Прием заявок до 28 января.

http://www.youtube.com/watch?v=00LoMxKY-_A#action=share
Для тех, кого заинтересовала тема машинного обучения :)

🎥 А? Машинное обучение
👁 1 раз ⏳ 533 сек.
Большое спасибо https://vas3k.ru/blog/machine_learning/
Без его картинок и примеров я бы не справился.

​Bayesian Deep Learning Benchmarks
Oxford Applied and Theoretical Machine Learning Group : https://github.com/OATML/bdl-benchmarks
#Bayesian #Benchmark #DeepLearning

🔗 OATML/bdl-benchmarks
Bayesian Deep Learning Benchmarks. Contribute to OATML/bdl-benchmarks development by creating an account on GitHub.

​"Everybody’s Talkin’: Let Me Talk as You Want"
Paper pdf: https://arxiv.org/pdf/2001.05201.pdf
Github: https://wywu.github.io/projects/EBT/EBT.html
Youtube: https://youtu.be/tNPuAnvijQk
This paper presents a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.

🔗

​The hyperparameter tuning problem in Bayesian Networks

🔗 The hyperparameter tuning problem in Bayesian Networks
In this history, we discuss the structural criteria to take into account when building models based on BN (Bayesian Network). We will…

​Integrate JupyterLab with Google Drive

🔗 Integrate JupyterLab with Google Drive
Automatically sync your Notebooks to the Cloud

🎥 VES104. Applications for Machine Learning in Streaming Video
👁 1 раз ⏳ 2810 сек.
John Dahl, Mux; Konstantin Wilms, Deluxe; Ben Dodson, Mux; Zac Shenker, CBS Interactive

🎥 VES103. Enhancing Media With Machine Learning In 2019
👁 1 раз ⏳ 2778 сек.
Jun Heider, CTOO, RealEyes Media

​SMOTE Oversampling for Imbalanced Classification with Python

🔗 SMOTE Oversampling for Imbalanced Classification with Python
Imbalanced classification involves developing predictive models on classification datasets that have a severe class imbalance. The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important. One approach …

​Open Questions about Generative Adversarial Networks
What we’d like to find out about GANs that we don’t know yet.

https://distill.pub/2019/gan-open-problems/

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Open Questions about Generative Adversarial Networks
What we'd like to find out about GANs that we don't know yet.

​Billion Dollar Data Science

🔗 Billion Dollar Data Science
How to prevent costly failures in high-risk data science applications