Search Results for "python q learning" - Page 3
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Showing 104 open source projects for "python q learning"
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JUDI.jl
Julia Devito inversion
...The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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Fondant
Production-ready data processing made easy and shareable
Fondant is a modular, pipeline-based framework designed to simplify the preparation of large-scale datasets for training machine learning models, especially foundation models. It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components.... -
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Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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sadsa
SADSA (Software Application for Data Science and Analytics)
SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code. -
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with... -
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Datapipe
Real-time, incremental ETL library for ML with record-level depend
Datapipe is a real-time, incremental ETL library for Python with record-level dependency tracking. Datapipe is designed to streamline the creation of data processing pipelines. It excels in scenarios where data is continuously changing, requiring pipelines to adapt and process only the modified data efficiently. This library tracks dependencies for each record in the pipeline, ensuring minimal and efficient data processing. -
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NZMATH
Python Calculator on Number Theory, three-birds-one learning material
NZMATH is a Python calculator on number theory. It is freely available and distributed under the BSD license. All programs are written only by Python so that you can easily see their algorithmic number theory. You can get NZMATH with a single command: % python -m pip install -U nzmath Here % is the command line prompt of Windows or Unix/macOS. -
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Personal Budget Tracker
Personal Budget Tracker — Simple Expense Logger
Personal Budget Tracker is a minimal yet functional CLI tool to help users manage their income and expenses. It supports categorization, listing, and CSV export. Great for learning Python or organizing your personal finances. -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is... -
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UAVs Predictive Maintenance
Vibration-Based Structural Health Monitoring of UAVs
This study presents an integrated software application that enables vibration-based structural health monitoring within a closed-loop Product Lifecycle Management (PLM) framework. The system collects time-domain vibration data from UAV components during the pre-flight phase and applies deep learning architectures—including Gated Recurrent Units (GRUs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs)—for accurate fault classification. Communication with the UAV is handled through the DroneKit-Python API, while RESTful APIs interface with the Aras Innovator PLM platform to automate data exchange and support predictive maintenance. ... -
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Kalshi-Quant-TeleBot
Kalshi Advanced Quantitative Trading Bot is an enterprise-grade
...Built with cutting-edge quantitative algorithms and professional risk management, it provides institutional-quality trading capabilities with user-friendly control The Kalshi Advanced Quantitative Trading Bot is a professional-grade automated trading system designed specifically for event-based markets on the Kalshi platform. This bot leverages advanced quantitative strategies, machine learning techniques, and real-time data analysis to identify profitable trading opportunities while maintaining robust risk management protocols. Built with a modular architecture, the system combines Python-based trading algorithms with a JavaScript Telegram bot interface for dynamic monitoring and interaction. The bot is designed to operate continuously, making data-driven decisions based on news sentiment analysis, statistical arbitrage opportunities -
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Ubix Linux
The Pocket Datalab
Ubix stands for Universal Business Intelligence Computing System. Ubix Linux is an open-source, Debian-based Linux distribution geared towards data acquisition, transformation, analysis and presentation. Ubix Linux purpose is to offer a tiny but versatile datalab. Ubix Linux is easily accessible, resource-efficient and completely portable on a simple USB key. Ubix Linux is a perfect toolset for learning data analysis and artificial intelligence basics on small to medium... -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
...Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. Take a look at a comparison picture and see how comfortably a TensorFlow/Python script translates into a C# program with TensorFlow.NET. -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the... -
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Algorithm Visualizer
Interactive Online Platform that Visualizes Algorithms from Code
...The README explains each example, lists required environment variables or credentials (e.g., Twilio/Gmail where applicable), and gives cron examples so readers can run the scripts in a real environment. The project is intentionally informal and educational: it’s meant for experimentation, learning language-interop, and having fun rather than production-grade automation. Many implementations exist across languages (shell, Ruby, Python, Node, PowerShell, Go, Java, and more) and contributors are encouraged to add further -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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TradingGym
Trading backtesting environment for training reinforcement learning
TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. -
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DeepH-pack
Deep neural networks for density functional theory Hamiltonian
DeepH-pack is the official implementation of the DeepH (Deep Hamiltonian) method described in the paper Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation and in the Research Briefing. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA and will support HONPAS. -
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Kinetic.jl
Universal modeling and simulation of fluid mechanics upon ML
Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs. -
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TradeMaster
TradeMaster is an open-source platform for quantitative trading
TradeMaster is a first-of-its-kind, best-in-class open-source platform for quantitative trading (QT) empowered by reinforcement learning (RL), which covers the full pipeline for the design, implementation, evaluation and deployment of RL-based algorithms. TradeMaster is composed of 6 key modules: 1) multi-modality market data of different financial assets at multiple granularities; 2) whole data preprocessing pipeline; 3) a series of high-fidelity data-driven market simulators for mainstream... -
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ScikitLearn.jl
Julia implementation of the scikit-learn API
The scikit-learn Python library has proven very popular with machine learning researchers and data scientists in the last five years. It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. ScikitLearn.jl brings these capabilities to Julia. Its primary goal is to integrate both Julia- and Python-defined models together into the scikit-learn framework. -
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GXSM
Scanning Probe Microscopy Controller and Data Visualization Software
GXSM -- Gnome X Scanning Microscopy: A multi-channel image and vector-probe data acquisition and visualization system designed for SPM techniques (STM,AFM..), but also SPA-LEED/LEED/LEEM data analysis. A plug-in interface allows any user add-on data-processing and special hardware and instrument support. Latest: NC-AFM and related explorative methods as SQDM can be configured. High-Speed external PAC-PLL hardware option with digital DSP link. Based on several hardware options it supports... -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant... -
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PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI.