Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
- autodiff
- deep-learning
- machine-learning
- rust
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A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- cookiecutter-data-science
- cookiecutter
- cookiecutter-template
- data-science
Flax is a neural network library for JAX that is designed for flexibility.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Fast, flexible and easy to use probabilistic modelling in Python.
- python
- machine-learning
- probabilistic-graphical-models
- pytorch
An Open Flexible Quadrotor Simulator
Fast, flexible and fun neural networks.
Pika H3 Flex: video and audio generation inference
Library for Fast and Flexible Human Pose Estimation
- tensorlayer
- tensorflow
- openpose
- pose-estimation
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.
- machine-learning
- python
- evolutionary-algorithms
- genetic-algorithm
Official code for Agent-Reflex: Teaching Language Agents to Act Reflectively
A simple, flexible, and extensible template for PyTorch. It's beautiful.
A flexible framework for neural network for Ruby
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning