HodaHisham

Hoda Hamdy's starred repositories

developer-roadmap

Interactive roadmaps, guides and other educational content to help developers grow in their careers.

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autocomplete

IDE-style autocomplete for your existing terminal & shell

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shap

A game theoretic approach to explain the output of any machine learning model.

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reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

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mockito

Most popular Mocking framework for unit tests written in Java

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or-tools

Google's Operations Research tools:

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learning-notes

Notes on books I read, talks I watch, articles I study, and papers I love

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Deep_reinforcement_learning_Course

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

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Data-Science-Interview-Questions-Answers

Curated list of data science interview questions and answers

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Pearl

A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.

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torchinfo

View model summaries in PyTorch!

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JuMP.jl

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

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awesome-ml4co

Awesome machine learning for combinatorial optimization papers.

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geonotebook

A Jupyter notebook extension for geospatial visualization and analysis

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Cbc

COIN-OR Branch-and-Cut solver

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PySCIPOpt

Python interface for the SCIP Optimization Suite

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METIS

METIS - Serial Graph Partitioning and Fill-reducing Matrix Ordering

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modeling-examples

Gurobi modeling examples

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neural-combinatorial-rl-pytorch

PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940

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imitation-learning

Imitation learning algorithms

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KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.

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