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
A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
🤖 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.
TranslatedLangGPT: Empowering everyone to become a prompt expert! 🚀 📌 Proposer of Structured Prompt 📌 Initiator of Meta-Prompt 📌 The most popular paradigm for putting prompts into practice | Language of GPT The pioneering framework for structured & meta-prompt design 10,000+ ⭐ | Battle-tested by thousands of users worldwide Created by 云中江树 (Yunzhong Jiangshu, "river and trees amid the clouds")
- prompt-engineering
- chatgpt
- deeplearning
- framework
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.
Playing Pokemon Red with Reinforcement Learning
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
- tensorflow
- machine-learning
- regression
- convolutional-neural-networks
Debugging, monitoring and visualization for Python Machine Learning and Data Science
A library for debugging/inspecting machine learning classifiers and explaining their predictions
- scikit-learn
- machine-learning
- xgboost
- lightgbm
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
Code for "High-Precision Model-Agnostic Explanations" paper
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
Mostly experiments based on "Advances in financial machine learning" book
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
⬛ Python Individual Conditional Expectation Plot Toolbox
- machine-learning
- interpretability
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.
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
- epigenomics
- pattern-search
- data-visualization
- interactive-machine-learning
Research experiments exploring uncommon quant techniques.
- analysis
- jupyter-notebook
- python
- quant
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
Exploring the space of drug combinations to discover synergistic drugs using Active Learning
Graviton: verify your TEAL program by experiment and observation
- algorand
- avm
- blockchain
- pyteal