Document Embeddings in Recommendation Systems
Talk by Jerry Chi, Data Science Manager at Indeed Tokyo. https://www.linkedin.com/in/jerrychi/
The talk includes:
* Brief overview of related concepts: Transformers, embeddings, and approximate nearest neighbors
* Using embeddings for retrieval vs. ranking
* Comparing production system architectures
* Comparing model architectures, fine-tuning vs. further pre-training
* Highlights of recent related research
Meetup: https://www.meetup.com/Machine-Learning-Tokyo/events/272175765/
=========================
MLT (Machine Learning Tokyo)
site: https://machinelearningtokyo.com/
github: https://github.com/Machine-Learning-Tokyo
slack: https://machinelearningtokyo.slack.com/messages
discuss: https://discuss.mltokyo.ai/
twitter: https://twitter.com/__MLT__
meetup: https://www.meetup.com/Machine-Learning-Tokyo/
facebook: https://www.facebook.com/machinelearningtokyo
Видео Document Embeddings in Recommendation Systems канала MLT Artificial Intelligence
The talk includes:
* Brief overview of related concepts: Transformers, embeddings, and approximate nearest neighbors
* Using embeddings for retrieval vs. ranking
* Comparing production system architectures
* Comparing model architectures, fine-tuning vs. further pre-training
* Highlights of recent related research
Meetup: https://www.meetup.com/Machine-Learning-Tokyo/events/272175765/
=========================
MLT (Machine Learning Tokyo)
site: https://machinelearningtokyo.com/
github: https://github.com/Machine-Learning-Tokyo
slack: https://machinelearningtokyo.slack.com/messages
discuss: https://discuss.mltokyo.ai/
twitter: https://twitter.com/__MLT__
meetup: https://www.meetup.com/Machine-Learning-Tokyo/
facebook: https://www.facebook.com/machinelearningtokyo
Видео Document Embeddings in Recommendation Systems канала MLT Artificial Intelligence
Показать
Комментарии отсутствуют
Информация о видео
27 августа 2020 г. 3:00:00
00:50:01
Другие видео канала
Intro to Sentence Embeddings with Transformers
Regression with categorical independent variables
Building Production Recommender Systems - Maciej Kula - WEB2DAY 2017
Using DLRM | Building Recommender Systems with PyTorch | Maxim Naumov and Dheevatsa Mudigere
Embeddings for Everything: Search in the Neural Network Era
Rasa Algorithm Whiteboard - Transformers & Attention 3: Multi Head Attention
Utilizing BERT to make keyword-based search smart -English version-
Improving DeepLabv3+using the Attention mechanism for Polyps in semantic segmentation
How to automatically extract major themes(Topics) from your text data |Python | NLP
Neural Collaborative Filtering (NCF) Explanation & Implementation in Pytorch
Extract Word Embedding & Sentence Embeddings from Text data using BERT
Deep Learning for Recommender Systems (Nick Pentreath)
LSTM in Keras | Understanding LSTM input and output shapes
Document Classification with Neo4j | Kenny Bastani, Developer Evangelist at Neo Technology
What is PyTorch | Building Recommender Systems with PyTorch | Joseph Spisak and Geeta Chauhan
Word Embeddings
Recurrent Neural Networks for Session-based Recommendations - Alexandros Karatzoglou
An Automatic Planning Recommender Transformer Model: optimizing task order and constraints in SCM
2.1.1. Hybrid Recommendation Systems
How does YouTube recommend videos? - AI EXPLAINED!