Mining of Massive Datasets
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
#book
#datamining
📝 4_123287577397560264.pdf - 💾3 052 181
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
#book
#datamining
📝 4_123287577397560264.pdf - 💾3 052 181
BoTorch: Bayesian Optimization in PyTorch
https://github.com/pytorch/botorch
🔗 BoTorch · Bayesian Optimization in PyTorch
Bayesian Optimization in PyTorch
https://github.com/pytorch/botorch
🔗 BoTorch · Bayesian Optimization in PyTorch
Bayesian Optimization in PyTorch
GitHub
GitHub - pytorch/botorch: Bayesian optimization in PyTorch
Bayesian optimization in PyTorch. Contribute to pytorch/botorch development by creating an account on GitHub.
Data Science for Startups: Containers
🔗 Data Science for Startups: Containers
Building reproducible setups for machine learning
🔗 Data Science for Startups: Containers
Building reproducible setups for machine learning
Towards Data Science
Data Science for Startups: Containers
Building reproducible setups for machine learning
A Gentle Introduction to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
🔗 A Gentle Introduction to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks. Some of the most important innovations have sprung from submissions by academics and industry leaders to the ImageNet Large Scale Visual Recognition Challenge, or ILSVRC. …
🔗 A Gentle Introduction to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks. Some of the most important innovations have sprung from submissions by academics and industry leaders to the ImageNet Large Scale Visual Recognition Challenge, or ILSVRC. …
MachineLearningMastery.com
A Gentle Introduction to the ImageNet Challenge (ILSVRC) - MachineLearningMastery.com
The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks.
Some of the most important innovations have sprung from submissions…
Some of the most important innovations have sprung from submissions…
Машинное обучение. МФТИ.
Лекция 1. Временные ряды: введение.
Лекция 2. Экспоненциальное сглаживание
Лекция 3. ARMA/ARIMA.
Доп. главы-4. Композиции алгоритмов,Иерархическое прогнозирование, Нейронные сети
Доп. главы. Лекция 5. Методы обучения ранжированию.
Доп. главы. Лекция 7. Тематическое моделирование
Доп.главы. Лекция 8. RL. Введение. Эволюционные алгоритмы
Доп. главы. Лекция 9. RL. Temporal Difference
Доп. главы. Лекция 10. Approximate reinforcement learning
🎥 Машинное обучение. Доп. главы. Лекция 1. Временные ряды введение.
👁 2836 раз ⏳ 4558 сек.
🎥 Машинное обучение: доп. главы 2. Экспоненциальное сглаживание
👁 79 раз ⏳ 4849 сек.
🎥 Машинное обучение: доп. главы 3. ARMA/ARIMA.
👁 55 раз ⏳ 4241 сек.
🎥 Машинное обучение: доп. главы 4. Композиции алгоритмов, Иерархическое прогнозирование
👁 42 раз ⏳ 4563 сек.
🎥 Машинное обучение: доп. главы 5. Методы обучения ранжированию.
👁 26 раз ⏳ 3937 сек.
🎥 Машинное обучение: доп. главы 7. Тематическое моделирование
👁 20 раз ⏳ 3873 сек.
🎥 Машинное обучение: доп.главы 8. RL. Введение. Эволюционные алгоритмы
👁 24 раз ⏳ 4861 сек.
🎥 Машинное обучение: доп. главы 9. RL. Temporal Difference
👁 25 раз ⏳ 3903 сек.
Лекция 1. Временные ряды: введение.
Лекция 2. Экспоненциальное сглаживание
Лекция 3. ARMA/ARIMA.
Доп. главы-4. Композиции алгоритмов,Иерархическое прогнозирование, Нейронные сети
Доп. главы. Лекция 5. Методы обучения ранжированию.
Доп. главы. Лекция 7. Тематическое моделирование
Доп.главы. Лекция 8. RL. Введение. Эволюционные алгоритмы
Доп. главы. Лекция 9. RL. Temporal Difference
Доп. главы. Лекция 10. Approximate reinforcement learning
🎥 Машинное обучение. Доп. главы. Лекция 1. Временные ряды введение.
👁 2836 раз ⏳ 4558 сек.
🎥 Машинное обучение: доп. главы 2. Экспоненциальное сглаживание
👁 79 раз ⏳ 4849 сек.
Лектор: Романенко А.А.🎥 Машинное обучение: доп. главы 3. ARMA/ARIMA.
👁 55 раз ⏳ 4241 сек.
Лектор: Романенко А.А.🎥 Машинное обучение: доп. главы 4. Композиции алгоритмов, Иерархическое прогнозирование
👁 42 раз ⏳ 4563 сек.
Лектор: Романенко А.А.🎥 Машинное обучение: доп. главы 5. Методы обучения ранжированию.
👁 26 раз ⏳ 3937 сек.
Лектор: Зухба А.В.🎥 Машинное обучение: доп. главы 7. Тематическое моделирование
👁 20 раз ⏳ 3873 сек.
Лектор: Зухба А.В.🎥 Машинное обучение: доп.главы 8. RL. Введение. Эволюционные алгоритмы
👁 24 раз ⏳ 4861 сек.
Лектор: Малых В.А.🎥 Машинное обучение: доп. главы 9. RL. Temporal Difference
👁 25 раз ⏳ 3903 сек.
Лектор: Малых В.А.🎥 Artificial Intelligence and Machine Learning in MSK Radiology
👁 1 раз ⏳ 3185 сек.
👁 1 раз ⏳ 3185 сек.
Howard Steinbach MD memorial lecture delivered by Dr. Beaulieu on April 17, 2019, at UCSF Medical Center. Includes a general tutorial on machine learning that many radiologists may find useful. Thanks to several colleagues acknowledged throughout the talk who shared slides!Vk
Artificial Intelligence and Machine Learning in MSK Radiology
Howard Steinbach MD memorial lecture delivered by Dr. Beaulieu on April 17, 2019, at UCSF Medical Center. Includes a general tutorial on machine learning that many radiologists may find useful. Thanks to several colleagues acknowledged throughout the talk…
Data Science for Startups: Containers
🔗 Data Science for Startups: Containers
Building reproducible setups for machine learning
🔗 Data Science for Startups: Containers
Building reproducible setups for machine learning
Towards Data Science
Data Science for Startups: Containers
Building reproducible setups for machine learning
Achieving a top 5% position in an ML competition with AutoML
🔗 Achieving a top 5% position in an ML competition with AutoML
AutoML pipelines are a hot topic. The general goal is simple: enable everyone to train high-quality models specific to their business…
🔗 Achieving a top 5% position in an ML competition with AutoML
AutoML pipelines are a hot topic. The general goal is simple: enable everyone to train high-quality models specific to their business…
Towards Data Science
Achieving a top 5% position in an ML competition with AutoML
AutoML pipelines are a hot topic. The general goal is simple: enable everyone to train high-quality models specific to their business…
🎥 Rise of Deep Learning - the driver of modern AI Online Webinar
👁 1 раз ⏳ 3794 сек.
👁 1 раз ⏳ 3794 сек.
ANNs are the most critical algorithms belonging to the most recent, and most sophisticated, branch of Machine Learning, called the Deep Learning.
Deep Learning and ANNs have revolutionized modern Artificial Intelligence and are responsible for incredible global disruption. Image Processors, Content Generators, Driverless Cars, Speech Assistants, Walking and Talking Robots, Stock Market Predictors and all such modern AI applications are driven by Deep Learning Neural Networks.
This session will introduce yVk
Rise of Deep Learning - the driver of modern AI Online Webinar
ANNs are the most critical algorithms belonging to the most recent, and most sophisticated, branch of Machine Learning, called the Deep Learning.
Deep Learning and ANNs have revolutionized modern Artificial Intelligence and are responsible for incredible…
Deep Learning and ANNs have revolutionized modern Artificial Intelligence and are responsible for incredible…
🎥 Tutorial 2019 || Deep Learning with Python, TensorFlow, and Keras tutorial
👁 1 раз ⏳ 1155 сек.
👁 1 раз ⏳ 1155 сек.
Tutorial 2019 || Deep Learning with Python, TensorFlow, and Keras tutorialVk
Tutorial 2019 || Deep Learning with Python, TensorFlow, and Keras tutorial
🎥 Tutorial 2019 || Cryptocurrency-predicting RNN intro - Deep Learning w/ Python, TensorFlow and Keras
👁 1 раз ⏳ 1234 сек.
👁 1 раз ⏳ 1234 сек.
Tutorial 2019 || Cryptocurrency-predicting RNN intro - Deep Learning w/ Python, TensorFlow and Keras p.8Vk
Tutorial 2019 || Cryptocurrency-predicting RNN intro - Deep Learning w/ Python, TensorFlow and Keras
Tutorial 2019 || Cryptocurrency-predicting RNN intro - Deep Learning w/ Python, TensorFlow and Keras p.8
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://towardsdatascience.com/the-relationship-between-biological-and-artificial-intelligence-aeaf5fb93e19?source=collection_home---4------1---------------------
🔗 The relationship between Biological and Artificial Intelligence
Critical review of the claims of brain/neuroscience inspiration in AI, especially Artificial Neural Networks
https://towardsdatascience.com/the-relationship-between-biological-and-artificial-intelligence-aeaf5fb93e19?source=collection_home---4------1---------------------
🔗 The relationship between Biological and Artificial Intelligence
Critical review of the claims of brain/neuroscience inspiration in AI, especially Artificial Neural Networks
PyTorch 1.1
https://github.com/pytorch/pytorch/releases/tag/v1.1.0
- Tensorboard (beta);
- DistributedDataParallel new functionality and tutorials;
- Multi-headed attention;
- EmbeddingBag enhancements;
- Other cool, but more niche features:
- nn.SyncBatchNorm;
- optim.lr_scheduler.CyclicLR;
🔗 pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch
https://github.com/pytorch/pytorch/releases/tag/v1.1.0
- Tensorboard (beta);
- DistributedDataParallel new functionality and tutorials;
- Multi-headed attention;
- EmbeddingBag enhancements;
- Other cool, but more niche features:
- nn.SyncBatchNorm;
- optim.lr_scheduler.CyclicLR;
🔗 pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch
GitHub
Release Official TensorBoard Support, Attributes, Dicts, Lists and User-defined types in JIT / TorchScript, Improved Distributed…
Note: CUDA 8.0 is no longer supported
Highlights
TensorBoard (currently experimental)
First-class and native support for visualization and model debugging with TensorBoard, a web application suite ...
Highlights
TensorBoard (currently experimental)
First-class and native support for visualization and model debugging with TensorBoard, a web application suite ...
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🔗 If you like to travel, let Python help you scrape the best fares!
A simple and customizable project with Python and Selenium, that will search for flights and send the prices directly to your email!
🔗 If you like to travel, let Python help you scrape the best fares!
A simple and customizable project with Python and Selenium, that will search for flights and send the prices directly to your email!
Towards Data Science
If you like to travel, let Python help you scrape the best fares!
A simple and customizable project with Python and Selenium, that will search for flights and send the prices directly to your email!
Perelman School of Medicine at the University of Pennsylvania
https://www.med.upenn.edu/urbslab/videos.html
🔗 Videos/Lectures | Urbanowicz Lab | Perelman School of Medicine at the University of Pennsylvania
Welcome to the URBS Lab (Unbounded Research in Biomedical Systems). Our primary goal is to develop, evaluate, and apply tools/strategies that can be leveraged to improve our understanding of human health and the strategies implemented to prevent, diagnose, and treat. This site aims to orient visitors to our past, present, and future research/goals as well as offer relevant resources and links.
https://www.med.upenn.edu/urbslab/videos.html
🔗 Videos/Lectures | Urbanowicz Lab | Perelman School of Medicine at the University of Pennsylvania
Welcome to the URBS Lab (Unbounded Research in Biomedical Systems). Our primary goal is to develop, evaluate, and apply tools/strategies that can be leveraged to improve our understanding of human health and the strategies implemented to prevent, diagnose, and treat. This site aims to orient visitors to our past, present, and future research/goals as well as offer relevant resources and links.
www.med.upenn.edu
Videos/Lectures | Urbanowicz Lab | Perelman School of Medicine at the University of Pennsylvania
Welcome to the URBS Lab (Unbounded Research in Biomedical Systems). Our primary goal is to develop, evaluate, and apply tools/strategies that can be leveraged to improve our understanding of human health and the strategies implemented to prevent, diagnose…
Открытый курс OpenDataScience и Mail. ru Group по машинному обучению
1. Pandas
2. Визуализация
3. Классификация, деревья решений
4. Логистическая регрессия
5. Случайный лес
6. Регрессия, работа с признаками
🎥 Лекция 1. Pandas. Открытый курс OpenDataScience по машинному обучению
👁 2900 раз ⏳ 7042 сек.
🎥 Лекция 2. Визуализация. Открытый курс OpenDataScience по машинному обучению
👁 623 раз ⏳ 7625 сек.
🎥 Untitled
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🎥 Untitled
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🎥 Лекция 5. Случайный лес. Открытый курс OpenDataScience по машинному обучению
👁 305 раз ⏳ 8595 сек.
🎥 Лекция 6. Регрессия, регуляризация. Открытый курс OpenDataScience по машинному обучению
👁 255 раз ⏳ 9833 сек.
1. Pandas
2. Визуализация
3. Классификация, деревья решений
4. Логистическая регрессия
5. Случайный лес
6. Регрессия, работа с признаками
🎥 Лекция 1. Pandas. Открытый курс OpenDataScience по машинному обучению
👁 2900 раз ⏳ 7042 сек.
For lectures in English, check out this playlist https://bit.ly/2zY6Xe2
То же видео, но с улучшенным звуком https://youtu.be/OAy96yiWohk (Denis Ce...🎥 Лекция 2. Визуализация. Открытый курс OpenDataScience по машинному обучению
👁 623 раз ⏳ 7625 сек.
То же видео, но с улучшенным звуком https://www.youtube.com/watch?v=uwQat1TV0JM (tnx to Denis Cera, Oleg Butko)
На 2-ой лекции мы попрактикуемся в...🎥 Untitled
👁 12 раз ⏳ 0 сек.
🎥 Untitled
👁 2 раз ⏳ 0 сек.
🎥 Лекция 5. Случайный лес. Открытый курс OpenDataScience по машинному обучению
👁 305 раз ⏳ 8595 сек.
То же видео, но с улучшенным звуком https://youtu.be/_XKQY62NJus (tnx to Denis Cera, Oleg Butko)
На 5-ой лекции обсудим любопытнейший вопрос – поч...🎥 Лекция 6. Регрессия, регуляризация. Открытый курс OpenDataScience по машинному обучению
👁 255 раз ⏳ 9833 сек.
То же видео, но с улучшенным звуком https://youtu.be/70WsnE4ep1Y (tnx to Denis Cera)
На 6-ой лекции обсудим задачу восстановления регрессии, как ...Vk
Лекция 1. Pandas. Открытый курс OpenDataScience по машинному обучению
For lectures in English, check out this playlist https://bit.ly/2zY6Xe2 То же видео, но с улучшенным звуком https://youtu.be/OAy96yiWohk (Denis Ce...
Spectral Inference Networks
https://arxiv.org/abs/1806.02215
http://github.com/deepmind/spectral_inference_networks
🔗 Spectral Inference Networks: Unifying Deep and Spectral Learning
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.
https://arxiv.org/abs/1806.02215
http://github.com/deepmind/spectral_inference_networks
🔗 Spectral Inference Networks: Unifying Deep and Spectral Learning
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.
arXiv.org
Spectral Inference Networks: Unifying Deep and Spectral Learning
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to...
Роман Логинов: Мультимоделирование как универсальный способ описания выборки общего вида
🔗 Роман Логинов: Мультимоделирование как универсальный способ описания выборки общего вида
В случае неоднородных данных в машинном обучении использования одной модели недостаточно. Для выявления этого используют комбинации нескольких моделей - муль...
🔗 Роман Логинов: Мультимоделирование как универсальный способ описания выборки общего вида
В случае неоднородных данных в машинном обучении использования одной модели недостаточно. Для выявления этого используют комбинации нескольких моделей - муль...
YouTube
Роман Логинов: Мультимоделирование как универсальный способ описания выборки общего вида
В случае неоднородных данных в машинном обучении использования одной модели недостаточно. Для выявления этого используют комбинации нескольких моделей - муль...