​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. …

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.
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.
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!

​Data Science for Startups: Containers

🔗 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…

🎥 Rise of Deep Learning - the driver of modern AI Online Webinar
👁 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 y

🎥 Tutorial 2019 || Deep Learning with Python, TensorFlow, and Keras tutorial
👁 1 раз ⏳ 1155 сек.
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.
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

​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

​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!

​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.

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.
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 ...

​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.

​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...

​https://m.youtube.com/watch?v=FDzIafdbTVw

🔗

​Policy Intelligence: Applying Machine Learning and Analytics to Security in GCP (Cloud Next '19)

🔗 Policy Intelligence: Applying Machine Learning and Analytics to Security in GCP (Cloud Next '19)
Cloud environments today are complex. Even when all the information is available, it’s hard to pull it all together to make a good decision. Imagine if your ...

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://towardsdatascience.com/careful-looking-at-your-model-results-too-much-can-cause-information-leakage-95b4517404bc?source=collection_home---4------4---------------------

🔗 Careful! Looking at Your Model Results Too Much Can Cause Information Leakage
It’s always to use as much data as you can when building machine learning models. I think we all are aware of the issue of overfitting…

​Make your own Super Pandas using Multiproc

🔗 Make your own Super Pandas using Multiproc
Increase your data preprocessing speed using parallelization in Pandas

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://machinelearningmastery.com/best-practices-for-preparing-and-augmenting-image-data-for-convolutional-neural-networks/

🔗 Best Practices for Preparing and Augmenting Image Data for Convolutional Neural Networks
It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of augmentation techniques during both the training and evaluation of the model. Instead of testing a wide range of options, a useful shortcut is to consider the types of …

🎥 Python Neural Networks - TensorFlow 2.0 Tutorial - What is a Neural Network?
👁 1 раз ⏳ 1629 сек.
This python neural network tutorial series will discuss how to use tensorflow 2.0 and provide tutorials on how to create neural networks with python and tensorflow. This specific video is the introduction video in the series and discusses what a neural network is.

Want a sneak peak into my life? Follow my Instagram @tech_with_tim where I'm going to be filming a video each morning sharing my goals for the day and what I have planned:
https://www.instagram.com/tech_with_tim

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