2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
Simple structured learning framework for python
Las características principales de pystruct/pystruct son: General Machine Learning, Machine Learning, Frameworks de Machine Learning, Machine Learning Packages.
Las alternativas de código abierto para pystruct/pystruct incluyen: danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python. aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… christophm/rulefit — Python implementation of the rulefit algorithm. davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… larsmans/seqlearn — Sequence learning toolkit for Python.
