This is a repo with links to everything you'd ever want to learn about data engineering
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This repository is maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org
- hacking
- penetration-testing
- hacking-series
- cybersecurity
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
- tensorflow
- machine-learning
- regression
- convolutional-neural-networks
Build a modern LLM from scratch. Every line commented. Explained like we are five.
- attention-mechanism
- deep-learning
- educational
- from-scratch
The repository is about 100+ python programming exercise problem discussed, explained, and solved in different ways
- python
- programming
- learning-exercise
- python-programming-exercises
📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
- football
- xg
- expected-goals
- statsbomb
List of papers, code and experiments using deep learning for time series forecasting
- time-series-forecasting
- lstm-neural-networks
- lstm
- deep-learning
Coding the Machine Learning Tutorial for Learning to Learn
Những kiến thức cần thiết để học tốt Machine Learning trong vòng 2 tháng. Essential Knowledge for learning Machine Learning in two months.
- machine-learning
- tensorflow
- tensorflow-tutorials
- tensorflow-experiments
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]
100 days of LLM inference engineering — daily posts, experiments, and visualizations
🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
- machine-learning
- javascript
- python
- tensorflow
ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
Mostly experiments based on "Advances in financial machine learning" book
Big Data essentials: Hadoop, MapReduce, Spark. Explore tutorials and demos in Jupyter notebooks—most are self-contained and live, ready to run with a click.