#计算机科学#Best Practices on Recommendation Systems
翻译 - 推荐系统的最佳做法
#面试#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)
#自然语言处理#OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
翻译 - OpenVINO™工具包存储库
#计算机科学# Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
#计算机科学#Fast Python Collaborative Filtering for Implicit Feedback Datasets
#计算机科学#A unified, comprehensive and efficient recommendation library
翻译 - 统一,全面,高效的推荐库
#计算机科学#Pytorch domain library for recommendation systems
翻译 - Pytorch domain library for recommendation systems
#计算机科学#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
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.
#计算机科学#A TensorFlow recommendation algorithm and framework in Python.
#计算机科学#An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
翻译 - 一个基于Tensorflow的基于深度学习的推荐开源工具包。
#计算机科学#NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
#自然语言处理#AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
#计算机科学#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
#计算机科学#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.
翻译 - 这是我们在RecSys 2019中发表的文章的资料库,``我们真的取得了很大进展吗?对最近的神经推荐方法的担忧分析''
#计算机科学#HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
A Comparative Framework for Multimodal Recommender Systems
#计算机科学#NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in p...
#计算机科学#key Deep Learning engineering tricks in recsys