| Name | Version | Summary | date |
| waffle-hub |
0.3.1 |
Waffle hub |
2024-01-22 02:17:08 |
| waffle-utils |
1.1.0 |
Waffle Utils 🥛 |
2024-01-22 00:12:56 |
| mlflow-saagie |
2.9.2 |
MLflow: A Platform for ML Development and Productionization - forked for Saagie |
2024-01-19 09:13:37 |
| luxonis-train |
0.0.1 |
Luxonis training framework for seamless training of various neural networks. |
2024-01-17 17:17:20 |
| waffle-dough |
0.0.0 |
Waffle Dough |
2024-01-16 13:07:21 |
| craft-text-detector-jsl |
0.4.3 |
Fast and accurate text detection library built on CRAFT implementation |
2024-01-15 18:00:17 |
| stream-infer |
0.4.2 |
Video streaming inference framework, integrating image algorithms and models for real-time/offline video structuring |
2024-01-12 06:20:29 |
| pymetamodels |
0.0.3 |
The pymetamodels package combines machine learning (ML) metamodeling and analysis tools for the virtual development of modeling systems within a common abstract framework implemented in an accessible and distributable Python package. This package is oriented to support ML applications in material science, material informatics and the construction of materials, components and systems soft metamodels informed by hard physics-based modelling and experimental characterisations. |
2024-01-09 14:35:00 |
| elemeno-ai-sdk |
0.6.17 |
A set of glue code and utilities to make using elemeno AI platform a smooth experience |
2024-01-05 20:03:48 |
| qdvis |
0.1.2 |
qdvis: Quick data visualizer. |
2024-01-03 08:27:23 |
| madewithclay |
0.0.5 |
SDK to use Clay, the AI for Earth |
2024-01-01 22:10:04 |
| mlkit-lyt |
0.0.3 |
Machine Learning in Python |
2023-12-28 20:26:55 |
| optscale-arcee |
0.1.37 |
ML profiling tool for OptScale |
2023-12-26 17:30:44 |
| ml-anim |
0.0.1 |
A simple library for building ML animations with Manim |
2023-12-26 09:05:16 |
| ml-verbs |
0.0.4 |
Generic interfaces for machine learning |
2023-12-23 09:11:47 |
| eyepop-sdk-python |
0.9.1 |
EyePop.ai Python SDK |
2023-12-22 20:40:54 |
| k-inertia |
1.0.0 |
K-Inertia: A User-Friendly Machine Learning Library K-Inertia or Kavish Inertia is a Python machine learning library designed for simplicity and ease of use. With a focus on user-friendly interfaces, K-Inertia provides implementations of various machine learning algorithms, including regression, logistic regression, k-nearest neighbors, naive Bayes, support vector machines, and k-means clustering. |
2023-12-16 07:13:18 |
| knertia |
0.0.5 |
K-Inertia: A User-Friendly Machine Learning Library K-Inertia or Kavish Inertia is a Python machine learning library designed for simplicity and ease of use. With a focus on user-friendly interfaces, K-Inertia provides implementations of various machine learning algorithms, including regression, logistic regression, k-nearest neighbors, naive Bayes, support vector machines, and k-means clustering. |
2023-12-15 20:20:19 |
| timepulse |
0.2.0 |
A set of tools to help with timeseries flow |
2023-12-15 13:43:54 |
| core-cortex |
0.5.0 |
Python API client for Core Cortex |
2023-12-13 14:46:35 |