Популярное

Музыка Кино и Анимация Автомобили Животные Спорт Путешествия Игры Юмор

Интересные видео

2025 Сериалы Трейлеры Новости Как сделать Видеоуроки Diy своими руками

Топ запросов

смотреть а4 schoolboy runaway турецкий сериал смотреть мультфильмы эдисон
dTub
Скачать

Fletcher Riehl: Using Embedding Layers to Manage High Cardinality Categorical Data | PyData LA 2019

Автор: PyData

Загружено: 2019-12-29

Просмотров: 7610

Описание:

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!
00:10 Help us add time stamps or captions to this video! See the description for details.

Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...

Fletcher Riehl: Using Embedding Layers to Manage High Cardinality Categorical Data | PyData LA 2019

Поделиться в:

Доступные форматы для скачивания:

Скачать видео mp4

  • Информация по загрузке:

Скачать аудио mp3

Похожие видео

A Bluffer's Guide to Dimension Reduction - Leland McInnes

A Bluffer's Guide to Dimension Reduction - Leland McInnes

Maciej Arciuch, Karol Grzegorczyk: Embeddings! Embeddings everywhere! | PyData London 2019

Maciej Arciuch, Karol Grzegorczyk: Embeddings! Embeddings everywhere! | PyData London 2019

CatBoost: тексты, эмбеддинги и предсказание неопределённости

CatBoost: тексты, эмбеддинги и предсказание неопределённости

089. Мастер класс Решение задач классификации при помощи CatBoost –  Никита Дмитриев

089. Мастер класс Решение задач классификации при помощи CatBoost – Никита Дмитриев

Richard Liaw: A Guide to Modern Hyperparameters Turning Algorithms | PyData LA 2019

Richard Liaw: A Guide to Modern Hyperparameters Turning Algorithms | PyData LA 2019

LLM и GPT - как работают большие языковые модели? Визуальное введение в трансформеры

LLM и GPT - как работают большие языковые модели? Визуальное введение в трансформеры

Jan van der Vegt: A walk through the isolation forest | PyData Amsterdam 2019

Jan van der Vegt: A walk through the isolation forest | PyData Amsterdam 2019

Hands On Data Science Project: Understand Customers with KMeans Clustering in Python

Hands On Data Science Project: Understand Customers with KMeans Clustering in Python

PyData Tel Aviv Meetup: SHAP Values for ML Explainability - Adi Watzman

PyData Tel Aviv Meetup: SHAP Values for ML Explainability - Adi Watzman

Kishan Manani - Feature Engineering for Time Series Forecasting | PyData London 2022

Kishan Manani - Feature Engineering for Time Series Forecasting | PyData London 2022

Vincent Warmerdam: Winning with Simple, even Linear, Models | PyData London 2018

Vincent Warmerdam: Winning with Simple, even Linear, Models | PyData London 2018

Embeddings for Everything: Search in the Neural Network Era

Embeddings for Everything: Search in the Neural Network Era

Aditya Lahiri: Dealing With Imbalanced Classes in Machine Learning | PyData New York 2019

Aditya Lahiri: Dealing With Imbalanced Classes in Machine Learning | PyData New York 2019

#DLDC2020 | Deep Learning Dev Con | Luca Massron - Deep Learning For Tabular Data

#DLDC2020 | Deep Learning Dev Con | Luca Massron - Deep Learning For Tabular Data

Gianluca Campanella: The unreasonable effectiveness of feature hashing | PyData London 2019

Gianluca Campanella: The unreasonable effectiveness of feature hashing | PyData London 2019

LSTM is dead. Long Live Transformers!

LSTM is dead. Long Live Transformers!

244 - What are embedding layers in keras?

244 - What are embedding layers in keras?

Encoding Categorical Data | Machine Learning Fundamentals

Encoding Categorical Data | Machine Learning Fundamentals

Tom Augspurger: Scalable Machine Learning with Dask | PyData New York 2019

Tom Augspurger: Scalable Machine Learning with Dask | PyData New York 2019

GraphRAG: союз графов знаний и RAG: Эмиль Эйфрем

GraphRAG: союз графов знаний и RAG: Эмиль Эйфрем

© 2025 dtub. Все права защищены.



  • Контакты
  • О нас
  • Политика конфиденциальности



Контакты для правообладателей: [email protected]