| yzhao062/pyod |
7,751 |
|
3 |
60 |
over 2 years ago |
90 |
November 18, 2023 |
189 |
bsd-2-clause |
Python |
| A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection) |
| achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project |
499 |
|
0 |
0 |
over 2 years ago |
0 |
|
4 |
mit |
|
| Complete-Life-Cycle-of-a-Data-Science-Project |
| anish-lakkapragada/SeaLion |
328 |
|
0 |
0 |
over 2 years ago |
113 |
May 08, 2022 |
0 |
apache-2.0 |
Python |
| The first machine learning framework that encourages learning ML concepts instead of memorizing class functions. |
| nla-group/classix |
85 |
|
0 |
0 |
over 2 years ago |
112 |
November 27, 2023 |
1 |
mit |
Python |
| Fast and explainable clustering in Python |
| andrewtavis/kwx |
57 |
|
0 |
0 |
over 2 years ago |
25 |
January 28, 2023 |
11 |
bsd-3-clause |
Python |
| BERT, LDA, and TFIDF based keyword extraction in Python |
| SigmaMonstR/data-science |
39 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
|
HTML |
| Lectures for Introduction to Data Science for Public Policy (PPOL 670-01) |
| sfme/RVAE_MixedTypes |
15 |
|
0 |
0 |
about 6 years ago |
0 |
|
0 |
mit |
Python |
| Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020) |
| sharmaroshan/Credit-Card-Fraud-Detection |
9 |
|
0 |
0 |
about 7 years ago |
0 |
|
0 |
gpl-3.0 |
Jupyter Notebook |
| It is Based on Anamoly Detection and by Using Deep Learning Model SOM which is an Unsupervised Learning Method to find patterns followed by the fraudsters. |
| arunp77/Machine-Learning |
7 |
|
0 |
0 |
about 2 years ago |
0 |
|
0 |
other |
Jupyter Notebook |
| Fundamentals & projects |
| rubenandrebarreiro/gpu-cuda-self-organising-maps |
7 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
mit |
C++ |
| 🧠 💡 📈 A project based in High Performance Computing. This project was built using CUDA (Compute Unified Device Architecture), C++ (C Plus Plus), C, CMake and JetBrains CLion. The scenario of the project was a GPU-based implementation of the Self-Organising-Maps (S.O.M.) algorithm for Artificial Neural Networks (A.N.N.), with the support of CUDA (Compute Unified Device Architecture), using its offered parallel optimisations and tunings. The final goal of the project was to test the several GPU-based implementations of the algorithm against a given CPU-based implementation of the same algorithm and, evaluate and compare the overall performance (speedup, efficiency and cost). |
