Search Results for "x11-basic" - Page 4
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Showing 267 open source projects for "x11-basic"
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Doctor Dignity
Doctor Dignity is an LLM that can pass the US Medical Licensing Exam
Doctor Dignity is a prototype project exploring how AI-assisted tooling might support compassionate, accessible health guidance for people who struggle to get timely care. The repository centers on a simple end-to-end pipeline—intake of user-reported symptoms, basic triage logic, and clear, supportive messaging—intended to demonstrate how such systems could be built. It emphasizes a humane UX: plain-language prompts, de-jargonized outputs, and guardrails that nudge users toward professional care when needed. The code is designed to be hackable rather than production-grade, giving learners a chance to experiment with NLP flows and lightweight back-end components. ... -
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ekho
Chinese text-to-speech engine
ekho is a project with relatively sparse documentation, but from the repository it appears to be a small-scale tool for audio processing and playback, possibly with features for speech synthesis or manipulation. The repo includes scripts and configuration files suggesting interactions with media/audio handling libraries. Because of limited README detail, it seems targeted at users comfortable reading and modifying code, rather than end users expecting polished UIs. The code structure implies... -
3
Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which significantly improves the model's ability to understand and execute instructions. -
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From Zero to Research Scientist guide
Detailed and tailored guide for undergraduate students
From-0-to-Research-Scientist-resources-guide is an open-source educational roadmap that helps learners progress from basic programming knowledge to becoming a research scientist in artificial intelligence. The repository focuses primarily on deep learning and natural language processing, providing structured guidance for individuals who want to pursue research careers in these fields. It compiles recommended courses, textbooks, tutorials, and academic resources needed to build expertise in machine learning research. ... -
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SmartGPT
A program that provides LLMs with ability to complete complex tasks
SmartGPT is an experimental autonomous agent framework built to help large language models tackle complex tasks with minimal or no additional user input. It works by decomposing larger objectives into smaller steps and gathering information from the internet and other outside sources as needed. The project is written in Rust and emphasizes modularity, allowing developers to compose different “Autos” depending on the workflow they want to build. Its architecture separates responsibility... -
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TF2DeepFloorplan
TF2 Deep FloorPlan Recognition using a Multi-task Network
TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab. This repo contains a basic procedure to train and deploy the DNN model suggested by the paper 'Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'. It rewrites the original codes from zlzeng/DeepFloorplan into newer versions of Tensorflow and Python. -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
minimalRL is a lightweight reinforcement learning repository that implements several classic algorithms using minimal PyTorch code. The project is designed primarily as an educational resource that demonstrates how reinforcement learning algorithms work internally without the complexity of large frameworks. Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation... -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. ... -
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pdf-extractor
Node.js module for rendering pdf pages to images, svgs and HTML files
...This can be used as a (transparent) layer over the image to enable text selection. Pdf text is extracted to a text file for different usages (e.g. indexing the text). This library is in it's most basic form a node.js wrapper for pdf.js. It has default renderers to generate a default output, but is easily extended to incorporate custom logic or to generate different output. It uses a node.js DOM and the node domstub from pdf.js do make pdf parsing available on node.js without a browser. -
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Elephas
Distributed Deep learning with Keras & Spark
Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Spark. Elephas currently supports a number of applications. Elephas brings deep learning with Keras to Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames. Keras... -
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nlpaug
Data augmentation for NLP
This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together. -
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Machine Learning Glossary
Machine learning glossary
...It covers a wide range of topics including neural networks, regression models, optimization techniques, loss functions, and evaluation metrics. The content is organized into sections that progressively introduce key ideas from basic machine learning concepts to more advanced mathematical topics. Many pages include diagrams or code examples to illustrate how algorithms work in practice. Because the project emphasizes accessibility, it is particularly useful for beginners who want a conceptual overview of machine learning terminology before diving into more technical research papers. -
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AcoPath for Java
Ant Colony Optimization algorithm for the shortest path problem.
...Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants. -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing... -
16
Google Research Football
Check out the new game server
Google Research Football is a reinforcement learning environment simulating soccer matches. It focuses on learning complex behaviors such as team collaboration and strategy formation in competitive settings. -
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Trax
Deep learning with clear code and speed
...Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. Trax includes basic models (like ResNet, LSTM, Transformer) and RL algorithms (like REINFORCE, A2C, PPO). It is also actively used for research and includes new models like the Reformer and new RL algorithms like AWR. Trax has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. You can use Trax either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. ... -
18
ReinforcementLearningAnIntroduction.jl
Julia code for the book Reinforcement Learning An Introduction
This project provides the Julia code to generate figures in the book Reinforcement Learning: An Introduction(2nd). One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl. -
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Jarvis Python AI Assistant
Python AI assistant
Jarvis is a voice commanding assistant service in Python 3.8 It can recognize human speech, talk to user and execute basic commands. Opens a web page (e.g 'Jarvis open youtube') Play music in Youtube (e.g 'Jarvis play mozart') Increase/decrease the speakers master volume (also can set max/mute speakers volume) (e.g 'Jarvis volume up!') Opens libreoffice suite applications (calc, writer, impress) (e.g 'Jarvis open calc') Tells about something, by searching on the internet (e.g 'Jarvis tells me about oranges') Tells the weather for a place (e.g 'Jarvis tell_the_skills me the weather in London') Tells the current time and/or date (e.g 'Jarvis tell me time or date') Set an alarm (e.g 'Jarvis create a new alarm') Tells the internet speed (ping, uplink and downling) (e.g 'Jarvis tell_the_skills me the internet speed') Tells the internet availability (e.g 'Jarvis is the internet connection ok?') ... -
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Teachable Machine
Explore how machine learning works, live in the browser
Teachable Machine is the open-source implementation of an experimental machine learning tool created by Google Creative Lab that allows users to train simple machine learning models directly in a web browser. The project demonstrates how neural networks can be trained interactively using images captured from a webcam or other inputs without requiring programming knowledge. Users can provide example images for different categories, and the system trains a model that learns to classify those... -
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MTBook
Machine Translation: Foundations and Models
This is a tutorial, the purpose is to introduce the basic knowledge and modeling methods of machine translation systematically, and on this basis, discuss some cutting-edge technologies of machine translation (formerly known as "Machine Translation: Statistical Modeling and Deep Learning") method"). Its content is compiled into a book, which can be used for the study of senior undergraduates and graduate students in computer and artificial intelligence related majors, and can also be used as reference material for researchers related to natural language processing, especially machine translation. ... -
22
Sklearn TensorFlow
Sklearn and TensorFlow: A Practical Guide to Machine Learning
...It focuses on teaching core machine learning concepts using Python while demonstrating practical workflows with popular libraries like Scikit-Learn and TensorFlow. The material covers topics ranging from basic machine learning theory to deep learning techniques and model evaluation, enabling learners to build and experiment with models step by step. -
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MASLua
Multi-agent system modeling with Lua
A framework to simulate systems of agents in Lua on a 2D grid map, with modules for describing agent behavior and communication. A working example of a taxi fleet is given. The "basic" version uses conventional belief-desire-intention module (BDI.lua) for agent programming and a textual I/O. The "basic_EFSSM" version uses only state-oriented programming for agents. (Available soon.) --- Ribas-Xirgo, Ll.: Multi-agent system model of taxi fleets. In Advances in Physical Agents II, Springer International Publishing, 2021. ... -
24
AcoPath
Ant Colony Optimization algorithm for the shortest path problem.
...Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants. Development takes place at https://github.com/zfoxer/AcoPath -
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PsoPath
Particle Swarm Optimization algorithm for the shortest path problem
...Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of Particle Swarm Optimization is the emulation of the social behaviour of, e.g., a flock of birds, as a stochastic optimisation method. Specifically, a particle is an entity representing a solution in the search space. Several particles cooperate inside an algorithmic flow to occupy positions close to the best solution. ...
