Optuna is an open-source hyperparameter optimization framework designed to automate the hyperparameter search process for machine learning models. It supports various frameworks like TensorFlow, Keras, Scikit-Learn, XGBoost, and LightGBM, offering features like eager search spaces, state-of-the-art algorithms, and easy parallelization.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
Multi-Label Classification with Deep Learning
Scikit-learn — the go-to library for machine learning offering a user friendly, consistent interface.
Pycaret — lowering the entry point for machine learning with low code, automated and end to end solutions.
PyTorch — build and deploy powerful, scalable neural networks with its highly flexible architecture.
TensorFlow — one of the most mature deep learning libraries, highly flexible and suited to a wide range of applications.
Keras — TensorFlow made simple.
FastAI — makes deep learning more accessible with a high-level API built on top of PyTorch.
Pycaret — lowering the entry point for machine learning with low code, automated and end to end solutions.
PyTorch — build and deploy powerful, scalable neural networks with its highly flexible architecture.
TensorFlow — one of the most mature deep learning libraries, highly flexible and suited to a wide range of applications.
Keras — TensorFlow made simple.
FastAI — makes deep learning more accessible with a high-level API built on top of PyTorch.
