Stanford Seminar - Erudite: Prototype System for Computational Intelligence
🔗 Stanford Seminar - Erudite: Prototype System for Computational Intelligence
Wen-mei Hwu University of Illinois, Urbana-Champaign January 16, 2018 Since the rise of deep learning in 2012, much progress has been made in deep-learning-b...
🔗 Stanford Seminar - Erudite: Prototype System for Computational Intelligence
Wen-mei Hwu University of Illinois, Urbana-Champaign January 16, 2018 Since the rise of deep learning in 2012, much progress has been made in deep-learning-b...
YouTube
Stanford Seminar - Erudite: Prototype System for Computational Intelligence
Wen-mei Hwu University of Illinois, Urbana-Champaign January 16, 2018 Since the rise of deep learning in 2012, much progress has been made in deep-learning-b...
https://habr.com/ru/company/dsec/blog/437092/
Безопасность алгоритмов машинного обучения. Атаки с использованием Python
#machinelearning #neuralnets #deeplearning #машинноеобучение
Наш телеграмм канал - https://t.me/ai_machinelearning_big_data
Безопасность алгоритмов машинного обучения. Атаки с использованием Python
#machinelearning #neuralnets #deeplearning #машинноеобучение
Наш телеграмм канал - https://t.me/ai_machinelearning_big_data
Хабр
Безопасность алгоритмов машинного обучения. Атаки с использованием Python
Машинное обучение активно применяется во многих областях нашей жизни. Алгоритмы помогают распознавать знаки дорожного движения, фильтровать спам, распознавать лица наших друзей на facebook, даже...
Pre-trained BERT in PyTorch
https://github.com/huggingface/pytorch-pretrained-BERT
🔗 huggingface/pytorch-pretrained-BERT
A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities. - huggingface/pytorch-pretrained-BERT
https://github.com/huggingface/pytorch-pretrained-BERT
🔗 huggingface/pytorch-pretrained-BERT
A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities. - huggingface/pytorch-pretrained-BERT
GitHub
GitHub - huggingface/transformers: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models…
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - GitHub - huggingface/t...
Обзор алгоритмов глубокого машинного обучения для роботов
Обзор будет полезен для тех, кто занимается физическими роботами и кому стало недостаточно arduino, а также для людей, кто хотел бы реализовать какие-либо из функций восприятия окружающего мира в своих роботах или устройствах.
https://habr.com/ru/post/435968/
#Искусственный интеллект,
#Машинноеобучение,
🔗 Обзор алгоритмов глубокого машинного обучения для роботов
Обзор будет полезен для тех, кто занимается физическими роботами и кому стало недостаточно arduino, а также для людей, кто хотел бы реализовать какие-либо из...
Обзор будет полезен для тех, кто занимается физическими роботами и кому стало недостаточно arduino, а также для людей, кто хотел бы реализовать какие-либо из функций восприятия окружающего мира в своих роботах или устройствах.
https://habr.com/ru/post/435968/
#Искусственный интеллект,
#Машинноеобучение,
🔗 Обзор алгоритмов глубокого машинного обучения для роботов
Обзор будет полезен для тех, кто занимается физическими роботами и кому стало недостаточно arduino, а также для людей, кто хотел бы реализовать какие-либо из...
Хабр
Обзор алгоритмов глубокого машинного обучения для роботов
Обзор будет полезен для тех, кто занимается физическими роботами и кому стало недостаточно arduino, а также для людей, кто хотел бы реализовать какие-либо из функций восприятия окружающего мира в...
Discovering and Classifying In-app Message Intent at Airbnb
Conversational AI is inspiring us to rethink the customer experience on our platform.
🔗 Discovering and Classifying In-app Message Intent at Airbnb
Conversational AI is inspiring us to rethink the customer experience on our platform.
Conversational AI is inspiring us to rethink the customer experience on our platform.
🔗 Discovering and Classifying In-app Message Intent at Airbnb
Conversational AI is inspiring us to rethink the customer experience on our platform.
Medium
Discovering and Classifying In-app Message Intent at Airbnb
Conversational AI is inspiring us to rethink the customer experience on our platform.
🎥 Android Studio 3.3, introducing Feast: a feature store for ML, NVIDIA Tesla T4 GPUs, & more!
👁 1 раз ⏳ 163 сек.
👁 1 раз ⏳ 163 сек.
TL;DR 142 | The Google Developer Show
Android Studio 3.3 → http://bit.ly/2Dy68KQ
Get your Android apps ready for 64-bit → http://bit.ly/2DyGllP
Feast: open source feature store for machine learning → http://bit.ly/2FJaK32
Go on Cloud Functions → http://bit.ly/2SbMzjS
NVIDIA Tesla T4 GPUs now in beta → http://bit.ly/2FM0tmX
Soft Actor-Critic: Deep Reinforcement Learning for Robotics → http://bit.ly/2UlpHfd
Google Summer of Code 2019 → http://bit.ly/2HxM3Z6
Here to bring you the latest developer news frVk
Android Studio 3.3, introducing Feast: a feature store for ML, NVIDIA Tesla T4 GPUs, & more!
TL;DR 142 | The Google Developer Show
Android Studio 3.3 → http://bit.ly/2Dy68KQ
Get your Android apps ready for 64-bit → http://bit.ly/2DyGllP
Feast: open source feature store for machine learning → http://bit.ly/2FJaK32
Go on Cloud Functions → http:…
Android Studio 3.3 → http://bit.ly/2Dy68KQ
Get your Android apps ready for 64-bit → http://bit.ly/2DyGllP
Feast: open source feature store for machine learning → http://bit.ly/2FJaK32
Go on Cloud Functions → http:…
Deep Mind vs Starcraft:
DeepMind won professional starcraft players!
video: https://www.youtube.com/watch?v=cUTMhmVh1qs&feature=youtu.be
article: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/
🎥 DeepMind StarCraft II Demonstration
👁 8 раз ⏳ 10370 сек.
DeepMind won professional starcraft players!
video: https://www.youtube.com/watch?v=cUTMhmVh1qs&feature=youtu.be
article: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/
🎥 DeepMind StarCraft II Demonstration
👁 8 раз ⏳ 10370 сек.
Join Artosis, RottterdaM and a cast of special guests for a unique StarCraft II showcase live from DeepMind in London, in partnership with Blizzard.YouTube
DeepMind StarCraft II Demonstration
Join Artosis, RottterdaM and a cast of special guests for a unique StarCraft II showcase live from DeepMind in London, in partnership with Blizzard.
🔥Лекции по Big Data
1 - BigData. Введение в машинное обучение
2 - BigData. Python
3 - BigData. Что такое BigData
4 - BigData. OLAP. What and why
5 - BigData. IoT и BigData
6 - BigData. Сhallenges of classification
7 - BigData. Formal Context Analysis
8 - BigData. Регрессия
9 - BigData. Хранение и анализ больших данных
10 - BigData. Deep learning
🎥 1 - BigData. Введение в машинное обучение
👁 603 раз ⏳ 1960 сек.
🎥 2 - BigData. Python
👁 651 раз ⏳ 8499 сек.
🎥 3 - BigData. Что такое BigData
👁 235 раз ⏳ 3792 сек.
🎥 4 - BigData. OLAP. What and why
👁 184 раз ⏳ 5766 сек.
🎥 5 - BigData. IoT и BigData
👁 97 раз ⏳ 4183 сек.
🎥 6 - BigData. Сhallenges of classification
👁 82 раз ⏳ 3923 сек.
🎥 7 - BigData. Formal Context Analysis
👁 71 раз ⏳ 6046 сек.
🎥 8 - BigData. Регрессия
👁 89 раз ⏳ 4118 сек.
🎥 9 - BigData. Хранение и анализ больших данных
👁 113 раз ⏳ 8210 сек.
🎥 10 - BigData. Deep learning
👁 104 раз ⏳ 5703 сек.
1 - BigData. Введение в машинное обучение
2 - BigData. Python
3 - BigData. Что такое BigData
4 - BigData. OLAP. What and why
5 - BigData. IoT и BigData
6 - BigData. Сhallenges of classification
7 - BigData. Formal Context Analysis
8 - BigData. Регрессия
9 - BigData. Хранение и анализ больших данных
10 - BigData. Deep learning
🎥 1 - BigData. Введение в машинное обучение
👁 603 раз ⏳ 1960 сек.
Лекция 1 - Введение в машинное обучение.
В лекции рассказывается о том, что подразумевается под понятием «машинное обучение» и какие задачи решаютс...🎥 2 - BigData. Python
👁 651 раз ⏳ 8499 сек.
Лекция 2 - Python, как язык анализа данных.
В лекции сделан небольшой обзор языков и программ для анализа данных. Рассказан базовый синтаксис языка...🎥 3 - BigData. Что такое BigData
👁 235 раз ⏳ 3792 сек.
Лекция 3 - Что такое BigData?
В лекции рассказывается о том, что же это такое. Цели, проблемы и практическая польза результатов
анализа BD на приме...🎥 4 - BigData. OLAP. What and why
👁 184 раз ⏳ 5766 сек.
Лекция 4 - OLAP. What and why. Lightning talk.
В лекции описание OLAP. Что это? Для чего? Каковы отличия от OLTP? Небольшой экскурс в анализ данных...🎥 5 - BigData. IoT и BigData
👁 97 раз ⏳ 4183 сек.
Лекция 5 - IoT and BigData
В лекции рассказывается о IoT and BigData. Области их пересечения, применения, основные проблемы и методы решения. Lambd...🎥 6 - BigData. Сhallenges of classification
👁 82 раз ⏳ 3923 сек.
Лекция 6 - Сhallenges of classification
The Internet is growing at a tremendous rate. The amount of information presented is beyond human comprehen...🎥 7 - BigData. Formal Context Analysis
👁 71 раз ⏳ 6046 сек.
Лекция 7 - Formal Concept Analysis
В этой лекции рассказывается о том, откуда возник анализ формальных понятий, для чего он используется и какие за...🎥 8 - BigData. Регрессия
👁 89 раз ⏳ 4118 сек.
Лекция 8 - Регрессия
В лекции рассказана задача регрессии на примере классической задачи предсказания цены дома в Силиконовой Долине. Также рассмот...🎥 9 - BigData. Хранение и анализ больших данных
👁 113 раз ⏳ 8210 сек.
Лекция 9 - Хранение и анализ больших данных
Лекция дает ответы на такие вопросы как: что такое большие данные, откуда они берутся, как их хранить, ...🎥 10 - BigData. Deep learning
👁 104 раз ⏳ 5703 сек.
Опубликовано: 19 февр. 2016 г.
Лекция 10 - Deep learning - нейронные сети и их применение.
Лекция рассказывает о истории возникновения и развития н...Vk
1 - BigData. Введение в машинное обучение
Лекция 1 - Введение в машинное обучение. В лекции рассказывается о том, что подразумевается под понятием «машинное обучение» и какие задачи решаютс...
Obtaining Insights From Data: Optimizing an NBA Career
https://towardsdatascience.com/obtaining-insights-from-data-optimizing-an-nba-career-6605c9f07119?source=collection_home---4------2---------------------
https://towardsdatascience.com/obtaining-insights-from-data-optimizing-an-nba-career-6605c9f07119?source=collection_home---4------2---------------------
Towards Data Science
Obtaining Insights From Data: Optimizing an NBA Career
Since the publication of Moneyball, people have started examining sports with more statistical scrutiny, so being a statistics-motivated…
https://habr.com/ru/company/hsespb/blog/437402/
Как я научила робота бегать по видео с YouTube
#machinelearning #neuralnets #deeplearning #машинноеобучение
Наш телеграмм канал - https://t.me/ai_machinelearning_big_data
Как я научила робота бегать по видео с YouTube
#machinelearning #neuralnets #deeplearning #машинноеобучение
Наш телеграмм канал - https://t.me/ai_machinelearning_big_data
Хабр
Как я научила робота бегать по видео с YouTube
Мы продолжаем рассказывать о совместных научных проектах наших студентов и JetBrains Research. В этой статье поговорим об алгоритмах глубокого обучения с подкреплением, которые используются для...
Understanding Machine Learning on Point Clouds through PointNet++
https://towardsdatascience.com/understanding-machine-learning-on-point-clouds-through-pointnet-f8f3f2d53cc3
🔗 Understanding Machine Learning on Point Clouds through PointNet++
Point clouds are a convenient way of representing spatial data and other unordered data. But what are they, and how are they used in ML?
https://towardsdatascience.com/understanding-machine-learning-on-point-clouds-through-pointnet-f8f3f2d53cc3
🔗 Understanding Machine Learning on Point Clouds through PointNet++
Point clouds are a convenient way of representing spatial data and other unordered data. But what are they, and how are they used in ML?
Medium
Understanding Machine Learning on Point Clouds through PointNet++
Point clouds are a convenient way of representing spatial data and other unordered data. But what are they, and how are they used in ML?
Deep Learning 2019 - Image classification
🎥 Lesson 1: Deep Learning 2019 - Image classification
👁 1 раз ⏳ 6012 сек.
🎥 Lesson 1: Deep Learning 2019 - Image classification
👁 1 раз ⏳ 6012 сек.
Note: please view this using the video player at http://course.fast.ai, instead of viewing on YouTube directly, to ensure you have the latest information. If you have questions, see if your question already has an answer by searching http://forums.fast.ai, and then post there if required.
The key outcome of lesson 1 is that we'll have trained an image classifier which can recognize pet breeds at state of the art accuracy. The key to this success is the use of *transfer learning*, which will be a key platfoVk
Lesson 1: Deep Learning 2019 - Image classification
Note: please view this using the video player at http://course.fast.ai, instead of viewing on YouTube directly, to ensure you have the latest information. If you have questions, see if your question already has an answer by searching http://forums.fast.ai…
Lesson 4: Deep Learning 2019 - NLP; Tabular data; Collaborative filtering; Embeddings
🔗 Lesson 4: Deep Learning 2019 - NLP; Tabular data; Collaborative filtering; Embeddings
In lesson 4 we'll dive in to *natural language processing* (NLP), using the IMDb movie review dataset. In this task, our goal is to predict whether a movie r...
🔗 Lesson 4: Deep Learning 2019 - NLP; Tabular data; Collaborative filtering; Embeddings
In lesson 4 we'll dive in to *natural language processing* (NLP), using the IMDb movie review dataset. In this task, our goal is to predict whether a movie r...
YouTube
Lesson 4: Deep Learning 2019 - NLP; Tabular data; Collaborative filtering; Embeddings
In lesson 4 we'll dive in to *natural language processing* (NLP), using the IMDb movie review dataset. In this task, our goal is to predict whether a movie r...
Fast Simulation with Generative Adversarial Networks
🔗 Fast Simulation with Generative Adversarial Networks
In this video, Dr. Sofia Vallecorsa from openlab at CERN presents: Fast Simulation with Generative Adversarial Networks. "This talk presents an approach base...
🔗 Fast Simulation with Generative Adversarial Networks
In this video, Dr. Sofia Vallecorsa from openlab at CERN presents: Fast Simulation with Generative Adversarial Networks. "This talk presents an approach base...
YouTube
Fast Simulation with Generative Adversarial Networks
In this video, Dr. Sofia Vallecorsa from openlab at CERN presents: Fast Simulation with Generative Adversarial Networks. "This talk presents an approach base...
In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał
🔗 In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał
PyData Warsaw 2018 Let's talk about In Browser AI - the open source educational project brings together Python & JavaScript. Bring deep learning demos of you...
🔗 In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał
PyData Warsaw 2018 Let's talk about In Browser AI - the open source educational project brings together Python & JavaScript. Bring deep learning demos of you...
YouTube
In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał
PyData Warsaw 2018 Let's talk about In Browser AI - the open source educational project brings together Python & JavaScript. Bring deep learning demos of you...
Lesson 6: Deep Learning 2019 - Regularization; Convolutions; Data ethics
🔗 Lesson 6: Deep Learning 2019 - Regularization; Convolutions; Data ethics
Today we discuss some powerful techniques for improving training and avoiding over-fitting: - *Dropout*: remove activations at random during training in orde...
🔗 Lesson 6: Deep Learning 2019 - Regularization; Convolutions; Data ethics
Today we discuss some powerful techniques for improving training and avoiding over-fitting: - *Dropout*: remove activations at random during training in orde...
YouTube
Lesson 6: Deep Learning 2019 - Regularization; Convolutions; Data ethics
Today we discuss some powerful techniques for improving training and avoiding over-fitting: - *Dropout*: remove activations at random during training in orde...
Lesson 7: Deep Learning 2019 - Resnets from scratch; U-net; Generative (adversarial) networks
🔗 Lesson 7: Deep Learning 2019 - Resnets from scratch; U-net; Generative (adversarial) networks
In the final lesson of Practical Deep Learning for Coders we'll study one of the most important techniques in modern architectures: the *skip connection*. Th...
🔗 Lesson 7: Deep Learning 2019 - Resnets from scratch; U-net; Generative (adversarial) networks
In the final lesson of Practical Deep Learning for Coders we'll study one of the most important techniques in modern architectures: the *skip connection*. Th...
YouTube
Lesson 7: Deep Learning 2019 - Resnets from scratch; U-net; Generative (adversarial) networks
In the final lesson of Practical Deep Learning for Coders we'll study one of the most important techniques in modern architectures: the *skip connection*. Th...
Understand the Impact of Learning Rate on Model Performance With Deep Learning Neural Networks
🔗 Understand the Impact of Learning Rate on Model Performance With Deep Learning Neural Networks
Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Choosing the learning rate is challenging as a value too small may result in a …
🔗 Understand the Impact of Learning Rate on Model Performance With Deep Learning Neural Networks
Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Choosing the learning rate is challenging as a value too small may result in a …
How to do Bayesian hyper-parameter tuning on a blackbox model
Optimization of arbitrary functions on Cloud ML Engine
🔗 How to do Bayesian hyper-parameter tuning on a blackbox model
Optimization of arbitrary functions on Cloud ML Engine
Optimization of arbitrary functions on Cloud ML Engine
🔗 How to do Bayesian hyper-parameter tuning on a blackbox model
Optimization of arbitrary functions on Cloud ML Engine
Towards Data Science
How to do Bayesian hyper-parameter tuning on a blackbox model
Optimization of arbitrary functions on Cloud ML Engine