🎥 Webinar: Question Answering and Virtual Assistants with Deep Learning
👁 1 раз ⏳ 3206 сек.
In this webinar, we’ll look at how Deep Learning can be used to create Question Answering (QA) and Virtual Assistant type systems.

You will learn about:
- Typical use cases of QA systems in finance, insurance, and ecommerce
- The power of neural search compared to traditional keyword search
- The challenges of large-scale neural search and how to overcome them

Presenters:
Sava Kalbachou, AI Research Engineer, Lucidworks
Andy Liu, Senior Data Scientist, Lucidworks
Justin Sears, VP of Product Market

✅!ВНИМАНИЕ!✅
Мы приглашаем АВТОРОВ студенческих работ по техническим и прикладным дисциплинам!
⛔Если Вы имеете опыт в написании рефератов, курсовых, дипломных работ, тогда Вам к нам!
⛔Если ты ответственный, пунктуальный и любишь заниматься написанием студенческих работ и получать за это гонорар, то тебе к НАМ!

👍🏻Мы предлагаем высокий заработок, свободный график работы и личный кабинет!

Звоните +7 (953)287-21-92 (вайбер, вотсасп)
Пишите raspred.tex5@yandex
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​Plotting business locations on maps using multiple Plotting libraries in Python

🔗 Plotting business locations on maps using multiple Plotting libraries in Python
Comparing Map Plotting libraries

​Activation Atlas

🔗 Activation Atlas
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned and what concepts it typically represents.

​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.