#计算机科学#Machine learning, in numpy
翻译 - 机器学习,以数字表示
#计算机科学#⚡机器学习实战(Python3):kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
#计算机科学#Gorse是一个用Go语言编写的开源推荐系统。Gorse的目标是成为一个通用的开源推荐系统,可以很容易地被引入到各种各样的在线服务中。通过将物品、用户和交互数据导入到Gorse中,系统将自动训练模型,为每个用户生成推荐。
#大语言模型#Postgres with GPUs for ML/AI apps.
#搜索#Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
#计算机科学#Easily compute clip embeddings and build a clip retrieval system with them
#搜索#JVector: the most advanced embedded vector search engine
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
#计算机科学#Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
#计算机科学#A curated list of Best Artificial Intelligence Resources
#计算机科学#TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
翻译 - TensorFlow Similarity 是一个 Python 包,专注于使相似性学习变得快速而简单。
#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''
⚡️⚡️⚡️《机器学习实战》代码(基于Python3)🚀
#计算机科学#PostgreSQL vector database extension for building AI applications
#计算机科学#Blazing fast framework for fine-tuning similarity learning models
#计算机科学#TOROS N2 - lightweight approximate Nearest Neighbor library which runs fast even with large datasets
Voice Conversion With Just Nearest Neighbors
#计算机科学#Pytorch、Scikit-learn实现多种分类方法,包括逻辑回归(Logistic Regression)、多层感知机(MLP)、支持向量机(SVM)、K近邻(KNN)、CNN、RNN,极简代码适合新手小白入门,附英文实验报告(ACM模板)
#算法刷题#⚠️ [ARCHIVED] This version has been archived as of october 2024 and will not be updated anymore, please refer to the README for a link to the new version. This is the official repository for the Recom...
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaus...