| apachecn/ailearning |
37,352 |
|
0 |
2 |
over 2 years ago |
8 |
March 20, 2022 |
1 |
other |
Python |
| AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2 |
| jasonlaska/spherecluster |
303 |
|
10 |
1 |
about 5 years ago |
5 |
November 13, 2018 |
11 |
mit |
Python |
| Clustering routines for the unit sphere |
| DwangoMediaVillage/pqkmeans |
249 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Fast and memory-efficient clustering |
| milesgranger/gap_statistic |
226 |
|
1 |
1 |
almost 2 years ago |
17 |
July 31, 2023 |
4 |
unlicense |
Rust |
| Dynamically get the suggested clusters in the data for unsupervised learning. |
| scitime/scitime |
105 |
|
0 |
0 |
about 5 years ago |
4 |
March 27, 2021 |
3 |
bsd-3-clause |
Python |
| Training time estimation for scikit-learn algorithms |
| zlxy9892/ml_code |
94 |
|
0 |
0 |
almost 4 years ago |
0 |
|
2 |
apache-2.0 |
Python |
| A repository for recording the machine learning code |
| datamole-ai/active-semi-supervised-clustering |
43 |
|
1 |
0 |
about 6 years ago |
1 |
September 18, 2018 |
3 |
mit |
Python |
| Active semi-supervised clustering algorithms for scikit-learn |
| analyticalmonk/KMeans_elbow |
35 |
|
0 |
0 |
almost 4 years ago |
0 |
|
1 |
mit |
Jupyter Notebook |
| Code for determining optimal number of clusters for K-means algorithm using the 'elbow criterion' |
| angristan/palette |
26 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
mit |
Python |
| Extract color palette from an image with k-means and k-NN // Project for the AI/ML class at Hanyang University |
| Cheng-Lin-Li/MachineLearning |
17 |
|
0 |
0 |
about 8 years ago |
0 |
|
1 |
gpl-3.0 |
Jupyter Notebook |
| Implementations of machine learning algorithm by Python 3 |
