| The-Art-of-Hacking/h4cker |
15,693 |
|
0 |
0 |
about 2 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more. |
| jdb78/pytorch-forecasting |
3,439 |
|
0 |
10 |
about 2 years ago |
34 |
July 26, 2020 |
464 |
mit |
Python |
| Time series forecasting with PyTorch |
| LongxingTan/Time-series-prediction |
762 |
|
0 |
0 |
over 2 years ago |
11 |
October 16, 2023 |
11 |
mit |
Python |
| tfts: Time series deep learning models in TensorFlow |
| jeffheaton/aifh |
735 |
|
0 |
0 |
over 7 years ago |
0 |
|
8 |
apache-2.0 |
Java |
| Artificial Intelligence for Humans |
| jrieke/awesome-machine-learning-startups-berlin |
205 |
|
0 |
0 |
about 4 years ago |
0 |
|
0 |
|
Python |
| 🤖 A curated list of machine learning & artificial intelligence startups in Berlin (Germany) |
| curiousily/Deep-Learning-For-Hackers |
196 |
|
0 |
0 |
almost 6 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT) |
| VJ-HLR-Developers/Half-Life-Resurgence |
64 |
|
0 |
0 |
about 2 years ago |
0 |
|
2 |
agpl-3.0 |
Lua |
| Recreation & expansion of NPCs, entities, and weapons from the Half-Life series into Garry's Mod! |
| datarobot-community/examples-for-data-scientists |
52 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
apache-2.0 |
Jupyter Notebook |
| mercure-imaging/mercure |
46 |
|
0 |
0 |
over 2 years ago |
0 |
|
10 |
mit |
JavaScript |
| mercure DICOM Orchestrator |
| ISTE-VESIT-ORG/Machinera-2020 |
19 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
mit |
|
| This is an AI Series where we will cover Machine Learning and Deep Learning topics from the very basics. |
