Kaggle Coffee Chat: Joel Grus | Kaggle
🔗 Kaggle Coffee Chat: Joel Grus | Kaggle
In this Coffee Chat Rachael talks with Joel Grus about software engineering best practices, whether they belong in data science, if you should use TensorFlow for fizzbuzz and, of course, why he doesn't like notebooks. You can follow Joel at https://twitter.com/joelgrus SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_confirmation=1&utm_medium=youtube&utm_source=channel&utm_campaign=yt-sub About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and
🔗 Kaggle Coffee Chat: Joel Grus | Kaggle
In this Coffee Chat Rachael talks with Joel Grus about software engineering best practices, whether they belong in data science, if you should use TensorFlow for fizzbuzz and, of course, why he doesn't like notebooks. You can follow Joel at https://twitter.com/joelgrus SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_confirmation=1&utm_medium=youtube&utm_source=channel&utm_campaign=yt-sub About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and
YouTube
Kaggle Coffee Chat: Joel Grus | Kaggle
In this Coffee Chat Rachael talks with Joel Grus about software engineering best practices, whether they belong in data science, if you should use TensorFlow...
Matrix processing with nanophotonics
🔗 Matrix processing with nanophotonics
Explanation of how to accelerate deep learning with photonic processors with comparisons to current digital electronics approaches.
🔗 Matrix processing with nanophotonics
Explanation of how to accelerate deep learning with photonic processors with comparisons to current digital electronics approaches.
Medium
Matrix processing with nanophotonics
Explanation of how to accelerate deep learning with photonic processors with comparisons to current digital electronics approaches.
A collection of datasets ready to use with TensorFlow
https://github.com/tensorflow/datasets
🔗 tensorflow/datasets
A collection of datasets ready to use with TensorFlow - tensorflow/datasets
https://github.com/tensorflow/datasets
🔗 tensorflow/datasets
A collection of datasets ready to use with TensorFlow - tensorflow/datasets
GitHub
GitHub - tensorflow/datasets: TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ... - tensorflow/datasets
🎥 Running our Reinforcement Learning Agent - Self-driving cars with Carla and Python p.5
👁 1 раз ⏳ 2376 сек.
👁 1 раз ⏳ 2376 сек.
Now that we've got our environment and agent, we just need to add a bit more logic to tie these together, which is what we'll be doing next to run our reinforcement learning self-driving agent.
Text-based tutorial and sample code: https://pythonprogramming.net/reinforcement-learning-self-driving-autonomous-cars-carla-python/
Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join
Discord: https://discord.gg/sentdex
Support the content: https://pythonprogramming.net/support-donateVk
Running our Reinforcement Learning Agent - Self-driving cars with Carla and Python p.5
Now that we've got our environment and agent, we just need to add a bit more logic to tie these together, which is what we'll be doing next to run our reinforcement learning self-driving agent.
Text-based tutorial and sample code: https://pythonprogramm…
Text-based tutorial and sample code: https://pythonprogramm…
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Authors: Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Sungja Choi, Inchul Hwang, Jihie Kim
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Abstract: Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations.
https://arxiv.org/abs/1908.10331
🔗 Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue hist
Authors: Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Sungja Choi, Inchul Hwang, Jihie Kim
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Abstract: Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations.
https://arxiv.org/abs/1908.10331
🔗 Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue hist
It’s a No Brainer: An Introduction to Neural Networks
🔗 It’s a No Brainer: An Introduction to Neural Networks
A gentle introduction to neural networks, now
🔗 It’s a No Brainer: An Introduction to Neural Networks
A gentle introduction to neural networks, now
Medium
It’s a No Brainer: An Introduction to Neural Networks
A gentle introduction to neural networks, now
Data Visualization GUIs с Dash и Python
Из данного видеокурса вы узнаете как создать интерфейсы визуализации интерактивных данных на основе браузера с Python и Dash.
1. Введение
2. Интерактивный пользовательский интерфейс
3. Динамический график на основе пользовательского ввода
4. Живые графики с событиями
5. Пример данных датчика транспортного средства Пример приложения
6. Анализ тональности в Python с помощью TextBlob и VADER Sentiment (+ Dash)
7. Потоковые твиты и тональности
8. Чтение из нашей базы данных тональности
9. Диаграмма тональности
🎥 Intro - Data Visualization GUIs with Dash and Python p.1
👁 1 раз ⏳ 1045 сек.
🎥 Interactive User Interface - Data Visualization GUIs with Dash and Python p.2
👁 1 раз ⏳ 497 сек.
🎥 Dynamic Graph based on User Input - Data Visualization GUIs with Dash and Python p.3
👁 1 раз ⏳ 991 сек.
🎥 Live Graphs with Events - Data Visualization GUIs with Dash and Python p.4
👁 1 раз ⏳ 1086 сек.
🎥 Vehicle sensor data App Example - Data Visualization GUIs with Dash and Python p.5
👁 1 раз ⏳ 1502 сек.
🎥 Sentiment Analysis in Python with TextBlob and VADER Sentiment (also Dash p.6)
👁 1 раз ⏳ 1405 сек.
🎥 Streaming Tweets and Sentiment - Data Visualization GUIs with Dash and Python p.7
👁 1 раз ⏳ 876 сек.
🎥 Reading from our sentiment database - Data Visualization GUIs with Dash and Python p.8
👁 1 раз ⏳ 317 сек.
🎥 Live Twitter Sentiment Graph - Data Visualization GUIs with Dash and Python p.9
👁 1 раз ⏳ 609 сек.
Из данного видеокурса вы узнаете как создать интерфейсы визуализации интерактивных данных на основе браузера с Python и Dash.
1. Введение
2. Интерактивный пользовательский интерфейс
3. Динамический график на основе пользовательского ввода
4. Живые графики с событиями
5. Пример данных датчика транспортного средства Пример приложения
6. Анализ тональности в Python с помощью TextBlob и VADER Sentiment (+ Dash)
7. Потоковые твиты и тональности
8. Чтение из нашей базы данных тональности
9. Диаграмма тональности
🎥 Intro - Data Visualization GUIs with Dash and Python p.1
👁 1 раз ⏳ 1045 сек.
How to create browser-based interactive data visualization interfaces with Python and Dash
Text tutorials and sample code: https://pythonprogrammi...🎥 Interactive User Interface - Data Visualization GUIs with Dash and Python p.2
👁 1 раз ⏳ 497 сек.
Welcome to part two of the Dash tutorial series for making interactive data visualization user interfaces with Python. In this tutorial, we're goin...🎥 Dynamic Graph based on User Input - Data Visualization GUIs with Dash and Python p.3
👁 1 раз ⏳ 991 сек.
Welcome to part three of the web-based data visualization with Dash tutorial series. Up to this point, we've learned how to make a simple graph and...🎥 Live Graphs with Events - Data Visualization GUIs with Dash and Python p.4
👁 1 раз ⏳ 1086 сек.
How to create live graphs in Python with Dash, the browser-based data visualization application framework.
Text tutorials and sample code: https:/...🎥 Vehicle sensor data App Example - Data Visualization GUIs with Dash and Python p.5
👁 1 раз ⏳ 1502 сек.
Welcome to part five of the data visualization apps in Python with Dash tutorial series. In this part, we're going to cover how to make the vehicle...🎥 Sentiment Analysis in Python with TextBlob and VADER Sentiment (also Dash p.6)
👁 1 раз ⏳ 1405 сек.
What's going on everyone and welcome to a quick tutorial on doing sentiment analysis with Python. Today, I am going to be looking into two of the m...🎥 Streaming Tweets and Sentiment - Data Visualization GUIs with Dash and Python p.7
👁 1 раз ⏳ 876 сек.
Hello and welcome to another tutorial with sentiment analysis, this time we're going to save our tweets, sentiment, and some other features to a da...🎥 Reading from our sentiment database - Data Visualization GUIs with Dash and Python p.8
👁 1 раз ⏳ 317 сек.
Hello and welcome to part 3 of our sentiment analysis visualization application project with Dash. Leading up to this part, we learned how to calcu...🎥 Live Twitter Sentiment Graph - Data Visualization GUIs with Dash and Python p.9
👁 1 раз ⏳ 609 сек.
Welcome to part 4 of our sentiment analysis application with Dash and Python. Next, we're going to tie everything together up to this point to crea...Vk
Intro - Data Visualization GUIs with Dash and Python p.1
How to create browser-based interactive data visualization interfaces with Python and Dash Text tutorials and sample code: https://pythonprogrammi...
19 сентября в Москве пройдет конференция по применению ИИ в юридической практике Legal AI. Конференция организована OpenTalks.AI вместе с European Legal Technology Association и Infotropic Media.
На конференции пройдут выступления лучших специалистов с реальными кейсами применения ИИ, сделан обзор текущих технологий и рассмотрены проблемы регулирования, этики и права.
Также, с утра пройдет завтрак и вводная лекция "ИИ на пальцах", а вечером панельная сессия с прогнозом развития технологий ИИ от "технологических звезд" отрасли!
Сайт конференции: http://legalai.ru/
🔗 Legal.AI
Конференция по применению искусственного интеллекта в юридической практике
На конференции пройдут выступления лучших специалистов с реальными кейсами применения ИИ, сделан обзор текущих технологий и рассмотрены проблемы регулирования, этики и права.
Также, с утра пройдет завтрак и вводная лекция "ИИ на пальцах", а вечером панельная сессия с прогнозом развития технологий ИИ от "технологических звезд" отрасли!
Сайт конференции: http://legalai.ru/
🔗 Legal.AI
Конференция по применению искусственного интеллекта в юридической практике
Kaggle Inclusive Images Challenge — Павел Остяков
https://www.youtube.com/watch?v=wT8XgTrcE1U
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🎥 Kaggle Inclusive Images Challenge — Павел Остяков
👁 1 раз ⏳ 2532 сек.
https://www.youtube.com/watch?v=wT8XgTrcE1U
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🎥 Kaggle Inclusive Images Challenge — Павел Остяков
👁 1 раз ⏳ 2532 сек.
Павел Остяков рассказывает про соревнование Kaggle Inclusive Images Challenge. Оно являлось частью NeurIPS 2018 competition track и Павел занял в нём первое место. Задача заключалась в классификации изображений c применением на новый географический регион.
Из видео вы сможете узнать:
- Про особенности задачи и датасета
- Ограничения в соревновании
- Ключевые идеи и подходы к решению
- Как достичь хороших результатов на Kaggle
Узнать о текущих соревнованиях можно на сайте http://mltrainings.ru/
УзнатьYouTube
Kaggle Inclusive Images Challenge — Павел Остяков
Павел Остяков рассказывает про соревнование Kaggle Inclusive Images Challenge. Оно являлось частью NeurIPS 2018 competition track и Павел занял в нём первое место. Задача заключалась в классификации изображений c применением на новый географический регион.…
🎥 2019.08.22 Александр Коротков - Machine learning решение за 4 месяца
👁 1 раз ⏳ 2764 сек.
👁 1 раз ⏳ 2764 сек.
I would like to talk about my experience of developing full stack ML solution for OCR(Optical character recognition). This is a small presentation about the task, solution and result of project.Vk
2019.08.22 Александр Коротков - Machine learning решение за 4 месяца
I would like to talk about my experience of developing full stack ML solution for OCR(Optical character recognition). This is a small presentation about the task, solution and result of project.
SQL Summer Camp: Nested & Repeated Data | Kaggle
🔗 SQL Summer Camp: Nested & Repeated Data | Kaggle
So far we've only looked at tables with a single value per cell... but what if your cells have multiple data? Or even entire nested data structures? 😱 Don't panic! Today we'll cover how to handle these like a pro. 💪 Course link: https://www.kaggle.com/learn/advanced-sql SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to g
🔗 SQL Summer Camp: Nested & Repeated Data | Kaggle
So far we've only looked at tables with a single value per cell... but what if your cells have multiple data? Or even entire nested data structures? 😱 Don't panic! Today we'll cover how to handle these like a pro. 💪 Course link: https://www.kaggle.com/learn/advanced-sql SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to g
YouTube
SQL Summer Camp: Nested & Repeated Data | Kaggle
So far we've only looked at tables with a single value per cell... but what if your cells have multiple data? Or even entire nested data structures? 😱 Don't ...
Basic Guide to Image Classification
🔗 Basic Guide to Image Classification
Understanding AI‘s ability to process images when you’ve never written a line of code before
🔗 Basic Guide to Image Classification
Understanding AI‘s ability to process images when you’ve never written a line of code before
Medium
Basic Guide to Image Classification
Understanding AI‘s ability to process images when you’ve never written a line of code before
Every Single Thing I Learned in a Data Science Boot Camp
🔗 Every Single Thing I Learned in a Data Science Boot Camp
Theory, hype, data, and models.
🔗 Every Single Thing I Learned in a Data Science Boot Camp
Theory, hype, data, and models.
Medium
Every Single Thing I Learned in a Data Science Boot Camp
Theory, hype, data, and models.
5 Minute Guide to Plotting with Pandas
🔗 5 Minute Guide to Plotting with Pandas
Find out how to quickly visualise data with this popular python tool
🔗 5 Minute Guide to Plotting with Pandas
Find out how to quickly visualise data with this popular python tool
Medium
5 Minute Guide to Plotting with Pandas
Find out how to quickly visualise data with this popular python tool
Towards creating AI with instincts
🔗 Towards creating AI with instincts
In late August 2019, researchers at Google released a paper titled Weight Agnostic Neural Networks, opening our eyes to a missing piece of…
🔗 Towards creating AI with instincts
In late August 2019, researchers at Google released a paper titled Weight Agnostic Neural Networks, opening our eyes to a missing piece of…
Medium
Towards creating AI with instincts
In late August 2019, researchers at Google released a paper titled Weight Agnostic Neural Networks, opening our eyes to a missing piece of…
🎥 Accelerate and Simplify Time Series Analysis and Forecasting with Amazon Forecast
👁 1 раз ⏳ 3174 сек.
👁 1 раз ⏳ 3174 сек.
Analyzing and forecasting time series data with traditional methods is a complex and time consuming process that often struggles to produce accurate results for large sets of irregular data by failing to combine it with other relevant independent variables. In this tech talk, we will explore how to accelerate this process by relying on deep learning with the new AI service Amazon Forecast. We will briefly review how the service works and jump into an end-to-end demonstration on a time series use case, divinVk
Accelerate and Simplify Time Series Analysis and Forecasting with Amazon Forecast
Analyzing and forecasting time series data with traditional methods is a complex and time consuming process that often struggles to produce accurate results for large sets of irregular data by failing to combine it with other relevant independent variables.…
AI For Everyone Free course from Andrew Ng
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
https://www.coursera.org/learn/ai-for-everyone
🔗 Искусственный интеллект для каждого | Coursera
Learn Искусственный интеллект для каждого from deeplearning.ai. AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In ...
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
https://www.coursera.org/learn/ai-for-everyone
🔗 Искусственный интеллект для каждого | Coursera
Learn Искусственный интеллект для каждого from deeplearning.ai. AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In ...
Coursera
AI For Everyone
Offered by DeepLearning.AI. AI is not only for ... Enroll for free.
🎥 Как защитить алгоритм машинного обучения от Adversarial-примеров | Технострим
👁 1 раз ⏳ 2202 сек.
👁 1 раз ⏳ 2202 сек.
Самые значимые и интересные доклады от наших партнеров - известных отраслевых конференций, теперь доступны на канале "Технострим". У нас вы найдете...Vk
Как защитить алгоритм машинного обучения от Adversarial-примеров | Технострим
Самые значимые и интересные доклады от наших партнеров - известных отраслевых конференций, теперь доступны на канале "Технострим". У нас вы найдете...
Apache NIFI — Краткий обзор возможностей на практике
https://habr.com/ru/post/465299/
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🔗 Apache NIFI — Краткий обзор возможностей на практике
Введение Так получилось, что на моем текущем месте работы мне пришлось познакомиться с данной технологией. Начну с небольшой предыстории. На очередном митинге, н...
https://habr.com/ru/post/465299/
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🔗 Apache NIFI — Краткий обзор возможностей на практике
Введение Так получилось, что на моем текущем месте работы мне пришлось познакомиться с данной технологией. Начну с небольшой предыстории. На очередном митинге, н...
Хабр
Apache NIFI — Краткий обзор возможностей на практике
Введение Так получилось, что на моем текущем месте работы мне пришлось познакомиться с данной технологией. Начну с небольшой предыстории. На очередном митинге, н...
Надстройка для Excel, облегчающая установку фильтров при работе с кубами (VBA)
Как известно, из коробки Excel не позволяет устанавливать фильтры по списку значений для сводных таблиц, а это ведь такая нужная вещь! Как отфильтровать товары по сотне кодов, а потом по другой сотне? #BigData #DataMining
https://habr.com/ru/post/457094/
🔗 Надстройка для Excel, облегчающая установку фильтров при работе с кубами (VBA)
Как известно, из коробки Excel не позволяет устанавливать фильтры по списку значений для сводных таблиц, а это ведь такая нужная вещь! Как отфильтровать товары п...
Как известно, из коробки Excel не позволяет устанавливать фильтры по списку значений для сводных таблиц, а это ведь такая нужная вещь! Как отфильтровать товары по сотне кодов, а потом по другой сотне? #BigData #DataMining
https://habr.com/ru/post/457094/
🔗 Надстройка для Excel, облегчающая установку фильтров при работе с кубами (VBA)
Как известно, из коробки Excel не позволяет устанавливать фильтры по списку значений для сводных таблиц, а это ведь такая нужная вещь! Как отфильтровать товары п...
Хабр
Надстройка для Excel, облегчающая установку фильтров при работе с кубами (VBA)
Как известно, из коробки Excel не позволяет устанавливать фильтры по списку значений для сводных таблиц, а это ведь такая нужная вещь! Как отфильтровать товары по сотне кодов, а потом по другой сотне?...