| Name | Version | Summary | date |
| c2-4apr24 |
0.1.2 |
Data Quality Check for Machine Learning |
2024-04-05 12:34:25 |
| e3x |
1.0.2 |
JAX-Library for building E(3)-equivariant deep learning architectures based on Flax. |
2024-04-05 09:29:18 |
| MDRMF |
0.0.6 |
Multidrug Resistance Machine Fishing |
2024-04-05 07:47:13 |
| tsflex |
0.4.0 |
Toolkit for flexible processing & feature extraction on time-series data |
2024-04-04 10:23:04 |
| opda |
0.6.1 |
Design and analyze optimal deep learning models. |
2024-04-04 03:41:54 |
| dlib |
19.24.4 |
A toolkit for making real world machine learning and data analysis applications |
2024-04-03 23:33:43 |
| nvidia-cuda-runtime-cu12 |
12.4.127 |
CUDA Runtime native Libraries |
2024-04-03 20:54:51 |
| nvidia-cuda-cccl-cu12 |
12.4.127 |
CUDA CCCL |
2024-04-03 20:54:40 |
| impactchart |
0.5.1 |
A package for generating impact charts. |
2024-04-03 17:30:27 |
| nvidia-nccl-cu11 |
2.21.5 |
NVIDIA Collective Communication Library (NCCL) Runtime |
2024-04-03 15:33:12 |
| nvidia-nccl-cu12 |
2.21.5 |
NVIDIA Collective Communication Library (NCCL) Runtime |
2024-04-03 15:32:57 |
| flwr |
1.8.0 |
Flower: A Friendly Federated Learning Framework |
2024-04-03 08:09:24 |
| nessai-models |
0.5.0 |
Models for nessai |
2024-04-02 11:22:05 |
| glasflow |
0.3.1 |
Normalising flows using nflows |
2024-04-02 10:11:52 |
| maq |
0.2 |
Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers. |
2024-04-01 23:15:17 |
| tsclassification |
1.1.1 |
A shapelet classifier for time series data. |
2024-04-01 21:43:17 |
| aitk |
2.0.1 |
Python tools for AI |
2024-04-01 19:50:59 |
| vqt |
0.1.3 |
Variable Q-Transform with PyTorch backend |
2024-04-01 19:32:48 |
| fcd |
1.2.2 |
Fréchet ChEMNet Distance |
2024-04-01 17:10:28 |
| search-in-a-third |
0.1.1 |
A Python package for efficient hyperparameter optimization in neural networks, using a greedy algorithm guided by heuristic directions. |
2024-04-01 10:38:06 |