​Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions

🔗 Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions
This content is part of a series about Chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville…

🎥 Improving TripAdvisor Photo Selection With Deep Learning
👁 3 раз ⏳ 914 сек.
#reworkDL

This presentation took place at the Deep Learning Summit, Boston 2018 and was given by Greg Amis, Principal Software Engineer at TripAdvisor.

For more presentations & interviews from the Deep Learning Summit, Boston 2018, head to the Video Hub here: http://videos.re-work.co/events/39-deep-learning-summit-boston-2018

🎥 Achieving Continuous Machine and Deep Learning with Apache Ignite and TensorFlow
👁 1 раз ⏳ 2794 сек.
To download the presentation slides, visit: https://www.gridgain.com/resources/technical-presentations/achieving-continuous-deep-and-machine-learning-apache-ignite-and

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.

🎥 Deep Learning на пальцах 11 - Аудио и распознавание речи (Юрий Бабуров)
👁 2 раз ⏳ 0 сек.

​April Edition: Reinforcement Learning

🔗 April Edition: Reinforcement Learning
How to Build an Actual Artificial Intelligence Agent

​Support Vector Machines — Soft Margin Formulation and Kernel Trick

🔗 Support Vector Machines — Soft Margin Formulation and Kernel Trick
Learn some of the advanced concepts that make Support Vector Machine a powerful linear classifier

​Deep Learning on Ancient DNA

🔗 Deep Learning on Ancient DNA
Reconstructing the Human Past with Deep Learning

​Python for Finance: Robo Advisor Edition

🔗 Python for Finance: Robo Advisor Edition
Extending Stock Portfolio Analyses and Dash by Plotly to track Robo Advisor-like Portfolios.

​Mining of Massive Datasets
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
#book
#datamining

📝 4_123287577397560264.pdf - 💾3 052 181

​Mining of Massive Datasets
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

​Data Science for Startups: Containers

🔗 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. …

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