TinyML

Repository

• 01 - Decision Tree

• 02 - Random Forest

• 03 - XGBoost Classifier

• 04 - GaussianNB

• 05 - Support Vector Machines

• 06 - SEFR

• 07 - Principal Component Analysis

• 08 - Binomial Logistic Regression

• 09 - K-means

• 10 - Linear Regression

• 11 - Gaussian Mixture Model

• 12 - Artificial Neural Networks (MLP)

• 13 - Convolutional Neural Networks (CNN)

• 14 - XGBoost Regression

• 15 - Poisson Regression

• 16 - K-Nearest Neighbors (KNN-Classifier)

• 17 - ElasticNet (ELNET)

• 18 - Long Short-Term Memory (LSTM)

• 19 - AutoEncoder

• 20 - Q-Learning

• 21 - Huber Regression

• 22 - Quantization Aware Training

• 23 - Post-Training Pruning

• 24 - Knowledge Distillation

• 25 - MicroMobileNet

• 26 - MultinominalNB (Text Classifier)

Medium - TinyML's Weekly Open Paper

• 01 - Decision Tree

• 02 - Random Forest

• 03 - XGBoost Classifier

• 04 - GaussianNB

• 05 - Support Vector Machines

• 06 - SEFR

• 07 - Principal Component Analysis

• 08 - Binomial Logistic Regression

• 09 - K-means

• 10 - Linear Regression

• 11 - Gaussian Mixture Model

• 12 - Artificial Neural Networks (MLP)

• 13 - Convolutional Neural Networks (CNN)

• 14 - XGBoost Regression

• 15 - Poisson Regression

• 16 - K-Nearest Neighbors (KNN-Classifier)

• 17 - ElasticNet (ELNET)