801 Repositories
Python Visualize-Optimization-Algorithms Libraries
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
AutoTrader AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. From sim
Second Order Optimization and Curvature Estimation with K-FAC in JAX.
KFAC-JAX - Second Order Optimization with Approximate Curvature in JAX Installation | Quickstart | Documentation | Examples | Citing KFAC-JAX KFAC-JAX
This repository contains the best Data Science free hand-picked resources to equip you with all the industry-driven skills and interview preparation kit.
Best Data Science Resources Hey, Data Enthusiasts out there! Finally, after lots of requests from the community I finally came up with the best free D
Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.
NEW RELEASE How Nebullvm Works • Tutorials • Benchmarks • Installation • Get Started • Optimization Examples Discord | Website | LinkedIn | Twitter Ne
🙌Kart of 210+ projects based on machine learning, deep learning, computer vision, natural language processing and all. Show your support by ✨ this repository.
ML-ProjectKart 📌 Repository This kart showcases the finest collection of all projects based on machine learning, deep learning, computer vision, natu
This repository contains implementations of all Machine Learning Algorithms from scratch in Python. Mathematics required for ML and many projects have also been included.
👏 Pre- requisites to Machine Learning
Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition:
Multi-Type-TD-TSR Check it out on Source Code of our Paper: Multi-Type-TD-TSR Extracting Tables from Document Images using a Multi-stage Pipeline for
A python library to artfully visualize Factorio Blueprints and an interactive web demo for using it.
Factorio Blueprint Visualizer I love the game Factorio and I really like the look of factories after growing for many hours or blueprints after tweaki
Developing and Comparing Vision-based Algorithms for Vision-based Agile Flight
DodgeDrone: Vision-based Agile Drone Flight (ICRA 2022 Competition) Would you like to push the boundaries of drone navigation? Then participate in the
Ana's Portfolio
Ana's Portfolio ✌️ Welcome to my Portfolio! You will find here different Projects I have worked on (from scratch) 💪 Projects 💻 1️⃣ Hangman game (Mad
clock_plot provides a simple way to visualize timeseries data, mapping 24 hours onto the 360 degrees of a polar plot
clock_plot clock_plot provides a simple way to visualize timeseries data mapping 24 hours onto the 360 degrees of a polar plot. For usage, please see
Code for intrusion detection system (IDS) development using CNN models and transfer learning
Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus
Python code to control laboratory hardware and perform Bayesian reaction optimization on the MIT Make-It system for chemical synthesis
Description This repository contains code accompanying the following paper on the Make-It robotic flow chemistry platform developed by the Jensen Rese
IEEE-WIE presents WIE Week of Code (WIEWoC), a 3-days long open-source contribution event starting from 1st March, highlighting different python spaces including web development, machine learning, game development, data structures and algorithms, and substantially more! We are creating an open source repository on Python along with all its applications!
WIE-WoC IEEE-WIE presents WIE Week of Code (WIEWoC), a 3-days long open-source contribution event starting from 1st March, highlighting different pyth
A toolkit for Lagrangian-based constrained optimization in Pytorch
Cooper About Cooper is a toolkit for Lagrangian-based constrained optimization in Pytorch. This library aims to encourage and facilitate the study of
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.
30-Days-of-ML-Kaggle 🔥 About the Hands On Program 💻 Machine learning beginner → Kaggle competitor in 30 days. Non-coders welcome The program starts
1000+ ready code templates to kickstart your next AI experiment
AI Seed Projects Start with ready code for your next AI experiment. Choose from 1000+ code templates, across a wide variety of use cases. All examples
Framework for evaluating ANNS algorithms on billion scale datasets.
Billion-Scale ANN http://big-ann-benchmarks.com/ Install The only prerequisite is Python (tested with 3.6) and Docker. Works with newer versions of Py
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools
Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t
Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset
Ego4D EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated v
Implementation of Basic Machine Learning Algorithms on small datasets using Scikit Learn.
Basic Machine Learning Algorithms All the basic Machine Learning Algorithms are implemented in Python using libraries Acknowledgements Machine Learnin
Crawl & visualize ICLR papers and reviews
Crawl and Visualize ICLR 2022 OpenReview Data Descriptions This Jupyter Notebook contains the data crawled from ICLR 2022 OpenReview webpages and thei
Soomvaar is the repo which 🏩 contains different collection of 👨💻🚀code in Python and 💫✨Machine 👬🏼 learning algorithms📗📕 that is made during 📃 my practice and learning of ML and Python✨💥
Soomvaar 📌 Introduction Soomvaar is the collection of various codes implement in machine learning and machine learning algorithms with python on coll
An end-to-end framework for mixed-integer optimization with data-driven learned constraints.
OptiCL OptiCL is an end-to-end framework for mixed-integer optimization (MIO) with data-driven learned constraints. We address a problem setting in wh
Visualize size of directories, s3 buckets.
Dir Sizer This is a work in progress, right now consider this an Alpha or Proof of Concept level. dir_sizer is a utility to visualize the size of a di
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.
sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or
This is an open source library implementing hyperbox-based machine learning algorithms
hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed
Athena is the only tool that you will ever need to optimize your portfolio.
Athena Portfolio optimization is the process of selecting the best portfolio (asset distribution), out of the set of all portfolios being considered,
In this project, RandomOverSampler and SMOTE algorithms were used to perform oversampling, ClusterCentroids algorithm was used to undersampling, SMOTEENN algorithm was applied as a combinatorial approach of over- and undersampling of credit card credit dataset from LendingClub. Machine learning models - BalancedRandomForestClassifier and EasyEnsembleClassifier were used to predict credit risk.
Overview of Credit Card Analysis In this project, RandomOverSampler and SMOTE algorithms were used to perform oversampling, ClusterCentroids algorithm
Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto
EasyMCDM - Quick Installation methods Install with PyPI Once you have created your Python environment (Python 3.6+) you can simply type: pip3 install
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M
Data Structures and Algorithms Python - Practice data structures and algorithms in python with few small projects
Data Structures and Algorithms All the essential resources and template code nee
Autolfads-tf2 - A TensorFlow 2.0 implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS
autolfads-tf2 A TensorFlow 2.0 implementation of LFADS and AutoLFADS. Installati
Python-Strongest-Encrypter - Transform your text into encrypted symbols using their dictionary
How does the encrypter works? Transform your text into encrypted symbols using t
Event-forecasting - Event Forecasting Algorithms With Python
event-forecasting Event Forecasting Algorithms Theory Correlating events in comp
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the
Kglab - an abstraction layer in Python for building knowledge graphs
Graph Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, RDFlib, pySHACL, RAPIDS, NetworkX, iGraph, PyVis, pslpython, pyarrow, etc.
This repository contnains sample problems with test cases using Cormen-Lib
Cormen Lib Sample Problems Description This repository contnains sample problems with test cases using Cormen-Lib. These problems were made for the pu
OMLT: Optimization and Machine Learning Toolkit
OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment.
LightGBM + Optuna: no brainer
AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin
Supplementary Data for Evolving Reinforcement Learning Algorithms
evolvingrl Supplementary Data for Evolving Reinforcement Learning Algorithms This dataset contains 1000 loss graphs from two experiments: 500 unique g
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size Xuanyi Dong, Lu Liu, Katarzyna Musial, Bogdan Gabrys in IEEE Transactions o
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.
Awesome AutoDL A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awe
QDax is a tool to accelerate Quality-Diveristy (QD) algorithms through hardware accelerators and massive parallelism
QDax: Accelerated Quality-Diversity QDax is a tool to accelerate Quality-Diveristy (QD) algorithms through hardware accelerators and massive paralleli
GeneDisco is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.
GeneDisco is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.
LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.
LibRerank LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRer
A Python implementation of red-black trees
Python red-black trees A Python implementation of red-black trees. This code was originally copied from programiz.com, but I have made a few tweaks to
Implementation of linesearch Optimization Algorithms in Python
Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti
CLASSIX is a fast and explainable clustering algorithm based on sorting
CLASSIX Fast and explainable clustering based on sorting CLASSIX is a fast and explainable clustering algorithm based on sorting. Here are a few highl
A small tool to test and visualize protein embeddings and amino acid proportions.
polyprotein_stats A small tool to test and visualize protein embeddings and amino acid proportions. Currently deployed on streamlit.io. Given a set of
BCI datasets and algorithms
Brainda Welcome! First and foremost, Welcome! Thank you for visiting the Brainda repository which was initially released at this repo and reorganized
An open-source hyper-heuristic framework for multi-objective optimization
MOEA-HH An open-source hyper-heuristic framework for multi-objective optimization. Introduction The multi-objective optimization technique is widely u
Visualize a molecule and its conformations in Jupyter notebooks/lab using py3dmol
Mol Viewer This is a simple package wrapping py3dmol for a single command visualization of a RDKit molecule and its conformations (embed as Conformer
Code infrastructure and player algorithms for the Codenames board game.
Codenames Code infrastructure and player algorithms for the Codenames board game. This is the active fork of mkali-personal/codenames. Intro This is b
Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters
Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.
FLIght SCheduling OPTimization - a simple optimization library for flight scheduling and related problems in the discrete domain
Fliscopt FLIght SCheduling OPTimization 🛫 or fliscopt is a simple optimization library for flight scheduling and related problems in the discrete dom
This repository is the code of the paper Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
ES_OTN_Public Carlos Güemes Palau, Paul Almasan, Pere Barlet Ros, Albert Cabellos Aparicio Contact us: [email protected], contactus@bn
Filtering variational quantum algorithms for combinatorial optimization
Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently.
Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them
Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them.
Crypto Portfolio Clustering with and without optimization techniques (elbow method, PCA).
Crypto Portfolio Clustering Crypto Portfolio Clustering with and without optimization techniques (elbow method, PCA). Analysis This is an anlysis of c
Balabobapy - Using artificial intelligence algorithms to continue the text
Balabobapy - Using artificial intelligence algorithms to continue the text
Conducted ANOVA and Logistic regression analysis using matplot library to visualize the result.
Intro-to-Data-Science Conducted ANOVA and Logistic regression analysis. Project ANOVA The main aim of this project is to perform One-Way ANOVA analysi
This repository provides some codes to demonstrate several variants of Markov-Chain-Monte-Carlo (MCMC) Algorithms.
Demo-of-MCMC These files are based on the class materials of AEROSP 567 taught by Prof. Alex Gorodetsky at University of Michigan. Author: Hung-Hsiang
Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)
Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed model for heart failure prediction accuracy of 88 percent.
Basic sort and search algorithms written in python.
Basic sort and search algorithms written in python. These were all developed as part of my Computer Science course to demonstrate understanding so they aren't 100% efficent
Genetic algorithms are heuristic search algorithms inspired by the process that supports the evolution of life.
Genetic algorithms are heuristic search algorithms inspired by the process that supports the evolution of life. The algorithm is designed to replicate the natural selection process to carry generation, i.e. survival of the fittest of beings.
Pytorch implementation of various High Dynamic Range (HDR) Imaging algorithms
Deep High Dynamic Range Imaging Benchmark This repository is the pytorch impleme
SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise
Automating reinforcement learning architecture design for code optimization. Che
An Empirical Review of Optimization Techniques for Quantum Variational Circuits
QVC Optimizer Review Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits". Each of the python files ca
RRT algorithm and its optimization
RRT-Algorithm-Visualisation This is a project that aims to develop upon the RRT
A machine learning model for Covid case prediction
CovidcasePrediction A machine learning model for Covid case prediction Problem Statement Using regression algorithms we can able to track the active c
This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize over continuous domains by Brandon Amos
Tutorial on Amortized Optimization This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize
This repo contains a powerful tool made using python which is used to visualize, analyse and finally assess the quality of the product depending upon the given observations
📈 Statistical Quality Control 📉 This repo contains a simple but effective tool made using python which can be used for quality control in statistica
Breast-Cancer-Classification - Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms
Breast-Cancer-Classification - Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms
Proyecto - Desgaste y rendimiento de empleados de IBM HR Analytics
Acceder al código desde Google Colab para poder ver de manera adecuada todas las visualizaciones y poder interactuar con ellas. Links de acceso: Noteb
Create an application to visualize single/multiple Xandar Kardian people counting sensors detection result for a indoor area.
Program Design Purpose: We want to create an application to visualize single/multiple Xandar Kardian people counting sensors detection result for a indoor area.
LINUX-AOS (Automatic Optimization System)
LINUX-AOS (Automatic Optimization System)
Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive losses
Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss
Fibonacci Method Gradient Descent
An implementation of the Fibonacci method for gradient descent, featuring a TKinter GUI for inputting the function / parameters to be examined and a matplotlib plot of the function and results.
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
Approximate Multiplier by HEAM What's HEAM? HEAM is a general optimization method to generate high-efficiency approximate multipliers for specific app
Improved Fitness Optimization Landscapes for Sequence Design
ReLSO Improved Fitness Optimization Landscapes for Sequence Design Description Citation How to run Training models Original data source Description In
Adaptable tools to make reinforcement learning and evolutionary computation algorithms.
Pearl The Parallel Evolutionary and Reinforcement Learning Library (Pearl) is a pytorch based package with the goal of being excellent for rapid proto
In the AI for TSP competition we try to solve optimization problems using machine learning.
AI for TSP Competition Goal In the AI for TSP competition we try to solve optimization problems using machine learning. The competition will be hosted
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization This repository contains the code for the BBI optimizer, introduced in the p
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.
shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t
A classification model capable of accurately predicting the price of secondhand cars
The purpose of this project is create a classification model capable of accurately predicting the price of secondhand cars. The data used for model building is open source and has been added to this repository. Most packages used are usually pre-installed in most developed environments and tools like collab, jupyter, etc. This can be useful for people looking to enhance the way the code their predicitve models and efficient ways to deal with tabular data!
Analyzing Earth Observation (EO) data is complex and solutions often require custom tailored algorithms.
eo-grow Earth observation framework for scaled-up processing in Python. Analyzing Earth Observation (EO) data is complex and solutions often require c
Final Project for Practical Python Programming and Algorithms for Data Analysis
Final Project for Practical Python Programming and Algorithms for Data Analysis (PHW2781L, Summer 2020) Redlining, Race-Exclusive Deed Restriction Lan
This is a python implementation of wordle, which uses the same set of available words as the hit game, Wordle
Wordle Game This is a python implementation of wordle, which uses the same set of available words as the hit game, Wordle. Play the game manually pyth
WebApp served by OAK PoE device to visualize various streams, metadata and AI results
DepthAI PoE WebApp | Bootstrap 4 & Vue.js SPA Dashboard Based on dashmin (https:
Scikit-event-correlation - Event Correlation and Forecasting over High Dimensional Streaming Sensor Data algorithms
scikit-event-correlation Event Correlation and Changing Detection Algorithm Theo
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion
A library for benchmarking, developing and deploying deep learning anomaly detection algorithms
A library for benchmarking, developing and deploying deep learning anomaly detection algorithms Key Features • Getting Started • Docs • License Introd
Tools for mathematical optimization region
Tools for mathematical optimization region
Cormen-Lib - An academic tool for data structures and algorithms courses
The Cormen-lib module is an insular data structures and algorithms library based on the Thomas H. Cormen's Introduction to Algorithms Third Edition. This library was made specifically for administering and grading assignments related to data structure and algorithms in computer science.
This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML)
package tests docs license stats support This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.