| Name | Version | Summary | date |
| safe-ds-examples |
0.5.0 |
Ready-to-use examples for the Safe-DS Python library. |
2023-03-26 17:55:06 |
| kangas |
2.2.1 |
Tool for exploring columnar data, including multimedia |
2023-03-26 17:53:58 |
| pySAR |
2.3.2 |
A Python package used to analysis Sequence Activity Relationships (SARs) of protein sequences and their mutants using Machine Learning. |
2023-03-26 17:35:00 |
| safe-ds |
0.4.0 |
A user-friendly library for Data Science in Python. |
2023-03-26 15:00:01 |
| torchx-nightly |
2023.3.26 |
TorchX SDK and Components |
2023-03-26 11:30:57 |
| transformers-stream-generator |
0.0.4 |
This is a text generation method which returns a generator, streaming out each token in real-time during inference, based on Huggingface/Transformers. |
2023-03-26 10:42:45 |
| keras-nightly |
2.13.0.dev2023032607 |
Deep learning for humans. |
2023-03-26 07:47:54 |
| mlms |
0.6.0 |
This package is to facilitate model selection in Machine Learning. |
2023-03-25 23:20:09 |
| sageworks |
0.1.2 |
SageWorks: An easy to use WorkBench for creating and deploying SageMaker Models |
2023-03-25 22:31:57 |
| learninghouse |
1.7.4 |
learningHouse - Teach your smart home everything |
2023-03-25 21:02:52 |
| plynx |
1.9.14 |
ML platform |
2023-03-25 20:48:44 |
| numerblox |
0.5.5 |
Tools for solid Numerai pipelines |
2023-03-25 19:24:34 |
| everywhereml |
0.2.21 |
Train ML in Python, run everywhere |
2023-03-25 16:07:10 |
| nlpcloud |
1.0.40 |
Python client for the NLP Cloud API |
2023-03-25 14:37:39 |
| watex |
0.2.0 |
Machine learning research in water exploration |
2023-03-25 07:12:31 |
| deepsparse-ent |
1.4.2 |
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application |
2023-03-25 03:06:50 |
| deepsparse |
1.4.2 |
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application |
2023-03-25 03:04:00 |
| sparseml |
1.4.4 |
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models |
2023-03-25 03:03:35 |
| matgl |
0.1.0 |
MatGL (Materials Graph Library) is a framework for graph deep learning for materials science. |
2023-03-24 21:17:39 |
| nhssynth |
0.1.3 |
Synthetic data generation pipeline leveraging a Differentially Private Variational Auto Encoder assessed using a variety of metrics |
2023-03-24 18:20:08 |