​Тренировка по машинному обучению 1 февраля

🔗 Тренировка по машинному обучению 1 февраля

​HiPlot: High-dimensional interactive plots made easy

https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/

Get it on GitHub:
https://github.com/facebookresearch/hiplot
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 HiPlot: High-dimensional interactive plots made easy
We are releasing HiPlot, a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data.

🎥 [ИТ-лекторий]: Яндекс.Маршрутизация: как IT-технологии улучшают логистические сервисы
👁 1 раз ⏳ 3586 сек.
Спикер: Даниил Тарарухин, руководитель группы аналитики в отделе B2BGeo геосервисов Яндекса

Описание:

Современные IT-технологии – Big Data, Machine Learning, облачные вычисления и прочие модные слова – постепенно выходят из мира Интернета и онлайн-сервисов в оффлайновый мир, стремясь освоить "классические" области экономики и сталкиваясь при этом со специфическими, порой весьма неожиданными проблемами.

Команда B2BGeo, входящая в геосервисы Яндекса (наверняка известные вам по "Яндекс.Картам" и "Навигатор

​StyleGAN2: Near-Perfect Human Face Synthesis...and More

🔗 StyleGAN2: Near-Perfect Human Face Synthesis...and More
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers Their blog post on street scene segmentation is available here: https://app.wandb.ai/borisd13/semantic-segmentation/reports/Semantic-Segmentation-on-Street-Scenes--VmlldzoxMDk2OA 📝 The paper "Analyzing and Improving the Image Quality of #StyleGAN" and its source code is available here: - http://arxiv.org/abs/1912.04958 - https://github.com/NVlabs/stylegan2 - https://colab.research.google.com/drive/1ShgW6wohEFQtqs_

​Автор рассказывает, как научил искусственный интеллект играть в Space Invaders.

https://hackernoon.com/how-i-trained-an-ai-to-play-atari-space-invaders-b3e8756ef026

🔗 How I Trained an AI to Play Atari Space Invaders

​Modeling the Risk of Traffic Accidents in New York City

🔗 Modeling the Risk of Traffic Accidents in New York City
Classifying risk of traffic accidents using a convLSTM2D deep learning model

​BlenderProc
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.

https://github.com/DLR-RM/BlenderProc

https://github.com/DLR-RM/BlenderProc4BOP

https://arxiv.org/abs/1911.01911v1

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 DLR-RM/BlenderProc
A procedural blender pipeline to generate images for deep learning - DLR-RM/BlenderProc

​Meena: Google’s New Chatbot

🔗 Meena: Google’s New Chatbot
A more human-like and versatile chatbot

​Robots are Ready for the Real World

🔗 Robots are Ready for the Real World
The new startup, Covariant, from UC Berkeley Artificial Intelligence Research shows the robots can do it in the big game (real world).

🎥 ml5.js: Train a Neural Network with Pixels as Input
👁 2 раз ⏳ 1118 сек.
This tutorial builds on ml5.neuralNetwork() videos examining raw pixels as inputs to a neural network. This sets the stage for a discussion on convolutional neural networks.

💻Code: https://thecodingtrain.com/Courses/ml5-beginners-guide/8.1-pixels-input.html
🎥Next video: coming soon!
🎥Beginners Guide to Machine Learning: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YPSwT06y_AEYTqIwbeam3y

🔗Starting code: https://editor.p5js.org/codingtrain/sketches/ARYvi6amN
🔗Regression: https://editor.p5js.org/codingt

🎥 MIT Deep Learning Basics 2020 Introduction and Overview
👁 1 раз ⏳ 4086 сек.

​Open Source Differentiable Computer Vision Library for PyTorch

https://kornia.org

Code: https://github.com/kornia/kornia

Paper: https://arxiv.org/abs/1910.02190v2
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 kornia/kornia
Open Source Differentiable Computer Vision Library for PyTorch - kornia/kornia

​Real-Time Sentiment Analytics and Visualization via ElectionTweetBoard

🔗 Real-Time Sentiment Analytics and Visualization via ElectionTweetBoard
This post will explore various forms of and considerations in data analytics, visualization, as well as Machine Learning by delving deeper…

​Топ-6 инструментов для анализа данных / data science.

https://youtu.be/23QtdnfhBRY

🔗 Top 6 Tool Types For Data Analysis / Data Science - Save hours by using the right tool
Quick Summary of YouTube Live stream Which Tools For Data Analytics / Data Science https://youtu.be/GD-JnuNS9gs R vs Python https://youtu.be/ETvvwTuiIps Data Wrangling with Excel https://www.youtube.com/playlist?list=PL8ncIDIP_e6uzAHkxmIqkHDAXkzoPWrDX

​Fast Video Object Segmentation using the Global Context Module.

http://arxiv.org/abs/2001.11243

🔗 Fast Video Object Segmentation using the Global Context Module
We developed a real-time, high-quality video object segmentation algorithm for semi-supervised video segmentation. Its performance is on par with the most accurate, time-consuming online-learning model, while its speed is similar to the fastest template-matching method which has sub-optimal accuracy. The core in achieving this is a novel global context module that reliably summarizes and propagates information through the entire video. Compared to previous approaches that only use the first, the last, or a select few frames to guide the segmentation of the current frame, the global context module allows us to use all past frames to guide the processing. Unlike the state-of-the-art space-time memory network that caches a memory at each spatiotemporal position, our global context module is a fixed-size representation that does not use more memory as more frames are processed. It is straightforward in implementation and has lower memory and computational costs than the space-time memory module. Equipped with the

​Project DeepSpeech
A TensorFlow implementation of Baidu's DeepSpeech architecture

Code: https://github.com/mozilla/DeepSpeech

Tensorflow & Pytorch: https://github.com/DemisEom/SpecAugment

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition:

https://arxiv.org/pdf/1904.08779.pdf

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 mozilla/DeepSpeech
A TensorFlow implementation of Baidu's DeepSpeech architecture - mozilla/DeepSpeech

​An Introduction to Unity ML-Agents

🔗 An Introduction to Unity ML-Agents
We’ll learn how Unity ML-Agents works and at the end of the article, you’ll train a RL agent to learn to jump over walls.

🎥 StyleGAN, Latent Space Interpolation - Week 2
👁 1 раз ⏳ 417 сек.
Learn more about machine learning for image makers by signing up at https://mailchi.mp/da905fbd76ee/machine-learning-artists

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​GitHub - BY571/Upside-Down-Reinforcement-Learning

https://github.com/BY571/Upside-Down-Reinforcement-Learning

🔗 BY571/Upside-Down-Reinforcement-Learning
Contribute to BY571/Upside-Down-Reinforcement-Learning development by creating an account on GitHub.