antgr's repositories

Linux-System-Monitoring-Library

lightweight c++ only linux monitoring library

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18337

18.337 - Parallel Computing and Scientific Machine Learning

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AA222FinalProject

Final Project for AA 222: Engineering Design Optimization: Multi-Objective Optimization for Sizing and Control of Microgrid Energy Storage

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algforopt-notebooks

Jupyter notebooks associated with the Algorithms for Optimization textbook

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algorithms-sedgewick-wayne

Solutions to all the exercises of the Algorithms book by Robert Sedgewick and Kevin Wayne

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alphafold

Open source code for AlphaFold.

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BayesianInferencePyMC

Workshop on Bayesian inference using PyMC

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C-Plus-Plus

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

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Cpp20

This is the code repository for my book "C++20 - Get the Details"

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cs101

Apprentice level course admin repo

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CS6208_2023

Advanced Topics in Artificial Intelligence, NUS CS6208, 2023

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dataHacker

code for dataHacker blog

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Forecasting-Time-Series-Data-with-Facebook-Prophet

Forecasting Time-Series Data with Facebook Prophet, published by Packt

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fourier

An Interactive Introduction to Fourier Transforms

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Graph-Data-Modeling-in-Python

Graph Data Modeling in Python, by Packt Publishing

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ML-From-Scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

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nn-zero-to-hero-notes

Jupyter Notebook notes on Andrej Karpathy's tutorial series, "Neural Networks: Zero to Hero."

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numpy-100

100 numpy exercises (with solutions)

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pyprobml

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

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Python

All Algorithms implemented in Python

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pytorch-image-classification

Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.

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resources

PyMC3 educational resources

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tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

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