Name | Version | Summary | date |
topaz-em |
0.3.5 |
Particle picking with positive-unlabeled CNNs |
2024-12-13 22:35:39 |
torchcnnbuilder |
0.1.4 |
Framework for the automatic creation of CNN architectures |
2024-10-15 17:10:44 |
conveiro |
0.2.1 |
Visualization of filters in convolutional neural networks |
2024-03-18 10:14:25 |
imgaug-denk |
0.4.4 |
Image augmentation library for deep neural networks |
2024-03-14 08:24:12 |
TSFEDL |
1.0.7.6 |
Time Series Spatio-Temporal Feature Extraction using Deep Learning |
2024-03-05 11:32:53 |
miso |
3.1.24 |
Python scripts for training CNNs for particle classification |
2024-02-20 11:52:58 |
pruningdistribution |
0.1.0 |
Pruning of CNNs with distributions |
2024-01-19 02:49:02 |
pyqlearning |
1.2.7 |
pyqlearning is Python library to implement Reinforcement Learning and Deep Reinforcement Learning, especially for Q-Learning, Deep Q-Network, and Multi-agent Deep Q-Network which can be optimized by Annealing models such as Simulated Annealing, Adaptive Simulated Annealing, and Quantum Monte Carlo Method. |
2023-12-26 03:00:08 |
miso2 |
3.0.7 |
Python scripts for training CNNs for particle classification |
2023-12-21 05:06:08 |
dmri-pcconv |
1.0.0 |
Parametric Continuous Convolution framework used for Diffusion MRI. |
2023-11-29 13:49:27 |
dmri-rcnn |
0.4.1 |
Diffusion MRI Recurrent CNN for Angular Super-resolution. |
2023-11-17 12:47:45 |
JakeSilbersteinMachineLearning |
0.2.14 |
Non-Functional Machine Learning Library |
2023-11-09 18:38:03 |
JakeSilberstein-ML |
0.2.11 |
Non-Functional Machine Learning Library |
2023-11-08 22:28:12 |
JSML |
0.2.10 |
Non-Functional Machine Learning Library |
2023-11-08 19:40:19 |
SegRunLib |
0.0.4 |
This is the module for segmentator running |
2023-11-06 23:35:58 |
torchcam |
0.4.0 |
Class activation maps for your PyTorch CNN models |
2023-10-19 18:42:32 |
torchy-nn |
0.3.5.3 |
NumPy based neural network package with PyTorch-like API |
2023-10-19 17:11:12 |
JSNN |
0.0.2 |
blank |
2023-10-03 00:33:05 |
ClarityAI |
1.0.0 |
ClarityAI is a Python package designed to empower machine learning practitioners with a wide range of interpretability methods to enhance the transparency and explainability of their ML models. |
2023-08-12 20:26:16 |
covxnet |
1.0.0 |
Official Implementation of CovXNet using Tensorflow 2.0 |
2023-07-23 16:42:53 |