Best Practices on Recommendation Systems
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Updated
May 16, 2024 - Python
Best Practices on Recommendation Systems
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
深度学习面试宝典(含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向)
Fast Python Collaborative Filtering for Implicit Feedback Datasets
A unified, comprehensive and efficient recommendation library
A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user
计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估
Pytorch domain library for recommendation systems
Learning materials, Quizzes & Assignment solutions for the entire IBM data science professional certification. Also included, a few resources that I found helpful.
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
Applying Data Science and Machine Learning to Solve Real World Business Problems
A TensorFlow recommendation algorithm and framework in Python.
推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction
HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
Collection of Artificial Intelligence projects.
深度学习在推荐系统中的应用及论文小结。
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
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