• ClipSaver
ClipSaver
Русские видео
  • Смешные видео
  • Приколы
  • Обзоры
  • Новости
  • Тесты
  • Спорт
  • Любовь
  • Музыка
  • Разное
Сейчас в тренде
  • Фейгин лайф
  • Три кота
  • Самвел адамян
  • А4 ютуб
  • скачать бит
  • гитара с нуля
Иностранные видео
  • Funny Babies
  • Funny Sports
  • Funny Animals
  • Funny Pranks
  • Funny Magic
  • Funny Vines
  • Funny Virals
  • Funny K-Pop

Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 скачать в хорошем качестве

Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 2 years ago

Python

Tutorial

Education

NumFOCUS

PyData

Opensource

learn

software

python 3

Julia

coding

learn to code

how to program

scientific programming

Не удается загрузить Youtube-плеер. Проверьте блокировку Youtube в вашей сети.
Повторяем попытку...
Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3
  • Поделиться ВК
  • Поделиться в ОК
  •  
  •  


Скачать видео с ютуб по ссылке или смотреть без блокировок на сайте: Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 в качестве 4k

У нас вы можете посмотреть бесплатно Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 или скачать в максимальном доступном качестве, видео которое было загружено на ютуб. Для загрузки выберите вариант из формы ниже:

  • Информация по загрузке:

Скачать mp3 с ютуба отдельным файлом. Бесплатный рингтон Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 в формате MP3:


Если кнопки скачивания не загрузились НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием видео, пожалуйста напишите в поддержку по адресу внизу страницы.
Спасибо за использование сервиса ClipSaver.ru



Hanna van der Vlis - Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3

Hanna van der Vlis Presents: Clusterf*ck: A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 At Apollo Agriculture, a Kenya based agro-tech startup, one of the challenging problems we face is to predict yields of Kenyan maize farmers. Like almost all data-sets, this data-set has a hierarchical structure: farmers within the same region aren’t independent. By ignoring this fact, a model could predict yields entirely from the region of the farmer, but fails to find any other meaningful insights, and we may not even realize. However, if we “overcorrected,” treating each region as completely separate, each individual analysis could be underpowered. Enter the hero of our story: Bayesian hierarchical modeling. Using a practical example in Pymc3, we’ll follow this hero as they identify and overcome clustered data-sets. Slides: https://pydata.org/london2022/wp-cont... www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...

Comments
  • Chris Fonnesbeck - Probabilistic Python: An Introduction to Bayesian Modeling with PyMC 2 years ago
    Chris Fonnesbeck - Probabilistic Python: An Introduction to Bayesian Modeling with PyMC
    Опубликовано: 2 years ago
    30462
  • The Bayesians are Coming to Time Series 4 years ago
    The Bayesians are Coming to Time Series
    Опубликовано: 4 years ago
    30214
  • Benjamin Vincent - What-if- Causal reasoning meets Bayesian Inference | PyData Global 2022 2 years ago
    Benjamin Vincent - What-if- Causal reasoning meets Bayesian Inference | PyData Global 2022
    Опубликовано: 2 years ago
    10695
  • Pedro Tabacof - Unlocking the Power of Gradient-Boosted Trees (using LightGBM) | PyData London 2022 2 years ago
    Pedro Tabacof - Unlocking the Power of Gradient-Boosted Trees (using LightGBM) | PyData London 2022
    Опубликовано: 2 years ago
    11976
  • Statistical Gold Nuggets | Bayesian Hierarchical Models 4 months ago
    Statistical Gold Nuggets | Bayesian Hierarchical Models
    Опубликовано: 4 months ago
    13324
  • Eyal Kazin - Hypothesis Testing Stop Criterion with 2 years ago
    Eyal Kazin - Hypothesis Testing Stop Criterion with "Precision Is The Goal" | PyData London 2022
    Опубликовано: 2 years ago
    663
  • Thomas Wiecki - Solving Real-World Business Problems with Bayesian Modeling | PyData London 2022 2 years ago
    Thomas Wiecki - Solving Real-World Business Problems with Bayesian Modeling | PyData London 2022
    Опубликовано: 2 years ago
    16721
  • Developing Hierarchical Models for Sports Analytics with Chris Fonnesbeck 1 year ago
    Developing Hierarchical Models for Sports Analytics with Chris Fonnesbeck
    Опубликовано: 1 year ago
    6313
  • Introduction to Bayesian data analysis - part 1: What is Bayes? 8 years ago
    Introduction to Bayesian data analysis - part 1: What is Bayes?
    Опубликовано: 8 years ago
    297009
  • Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3  | PyData London 2019 5 years ago
    Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3 | PyData London 2019
    Опубликовано: 5 years ago
    24104

Контактный email для правообладателей: [email protected] © 2017 - 2025

Отказ от ответственности - Disclaimer Правообладателям - DMCA Условия использования сайта - TOS