Popular GitHub Repositories for Pca

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Book_5_《统计至简》 | 鸢尾花书：从加减乘除到机器学习；上架！

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

H2Oai GPU Edition

Using advanced control and computer vision techniques in an easy way for embedded

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

Create animations for the optimization trajectory of neural nets

Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.

In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

machine learning algorithms in Swift

This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showcasing the implementation and comparison of different machine learning models for binary and multi-class classification tasks.

Federated Principal Component Analysis Revisited!

zeta-lean: minimalistic python machine learning library built on top of numpy and matplotlib

? All of courses, assignments, exercises, mini-projects and books that I've done so far in the process of learning by myself Machine Learning and Data Science.

Animated Visualizations of Popular Machine Learning Algorithms

? A model for early detection of multiple faults in induction motors based on the use of PCA and multilabel decision-trees

Scalable Ultra-Sparse Bayesian PCA