Sequence learning toolkit for Python
Las características principales de larsmans/seqlearn son: General Machine Learning, Machine Learning, Frameworks de Machine Learning, Machine Learning Packages.
Las alternativas de código abierto para larsmans/seqlearn 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… lensacom/sparkit-learn — PySpark + Scikit-learn = Sparkit-learn.
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
