🎥 Object Detection with Deep Learning - Andreu Girbau - UPC TelecomBCN Barcelona 2019
👁 1 раз ⏳ 1254 сек.
https://telecombcn-dl.github.io/2019-dlcv/

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand

🎥 Face Recognition with Deep Learning - Ramon Morros - UPC TelecomBCN Barcelona 2019
👁 1 раз ⏳ 1921 сек.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This cou

​Книга Deep Learning by Ian Goodfellow

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Открыть в Telegram



📝 Deep Learning - Ian Goodfello, Yoshua Bengio & Aaron Courville.pdf - 💾22 717 311

​📚Новая книга Нассима Талеба

Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications

https://arxiv.org/abs/2001.10488

🔗 Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications
The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible. Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress". Traditional asymptotics deal mainly with either n=1 or $n=\infty$, and the real world is in between, under of the "laws of the medium numbers" --which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: + The sample mean is rarely in line with the population mean, with effect on "naive empiricism", but can be sometimes be estimated via parametric methods. + The "empirical distribution" is rarely empirical. + Parameter uncertainty has compounding effects on statistical metrics. + Dimension reduction (principal components)


📝 Statistical Consequences of Fat Tails- Real World Preasymptotics, Epistemology, and Applications.pdf - 💾28 601 829

​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