Search Results for "learning" - Page 78
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Showing 4819 open source projects for "learning"
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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 at once. ... -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
...The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. Because the code is intentionally lightweight, it is often used as a teaching resource for students learning about natural language processing and deep learning architectures. Developers can explore the repository to understand how language models generate text and how transformer components interact within the architecture. -
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javaboy-code-samples
Collection of Java code examples and demo projects
...Rather than focusing on a single application, it groups many small sample programs and projects that exemplify usage of Java core APIs, Spring Boot, frameworks like GRPC and Shiro, REST services, JWT, web security, caching, asynchronous processing, and database interactions. Each sample serves as a compact learning unit for a specific technology or pattern, allowing developers to explore one concern at a time without navigating a massive codebase. The repository often accompanies blog posts and tutorials, helping developers learn by reading articles and examining the corresponding code side-by-side. Because these practical examples cover common real-world problems and solutions, the project has amassed significant developer interest and engagement. -
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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. -
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ThinkJulia.jl
Port of the book Think Python to the Julia programming language
ThinkJulia.jl is an open source educational project that adapts Think Python by Allen B. Downey into the Julia programming language, with contributions by Ben Lauwens. It provides a comprehensive introduction to programming and computational thinking using Julia’s modern, high-performance features. The book is structured to gradually teach core concepts such as variables, control flow, functions, recursion, object-oriented programming, and data structures, while offering hands-on exercises... -
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ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
...The collection provides a variety of plugin implementations that showcase different use cases, helping developers learn how to extend ChatGPT’s functionality. It is designed to serve both as a learning resource for beginners and a reference point for more experienced developers. By centralizing community contributions, the repository highlights practical applications of plugins across domains such as productivity, data access, and automation. The project also serves as a starting point for developers interested in building their own custom plugins, offering inspiration and code samples. ... -
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bugstack
Java Backend Development Skills Roadbook
RoadMap is a Java backend development learning roadmap created to help programmers understand the full skill path from basic computing knowledge to delivery and deployment. It organizes the learning journey around the real workflow of taking requirements, designing systems, building software, testing quality, and releasing applications. The roadmap covers computer fundamentals, programming foundations, architecture, environment setup, backend development, libraries, debugging, testing, quality analysis, and deployment. ... -
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LlamaChat
Chat with your favourite LLaMA models in a native macOS app
Chat with your favourite LLaMA models, right on your Mac. LlamaChat is a macOS app that allows you to chat with LLaMA, Alpaca, and GPT4All models all running locally on your Mac. -
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llm
An ecosystem of Rust libraries for working with large language models
llm is an ecosystem of Rust libraries for working with large language models - it's built on top of the fast, efficient GGML library for machine learning. The primary entry point for developers is the llm crate, which wraps the llm-base and the supported model crates. Documentation for the released version is available on Docs.rs. For end-users, there is a CLI application, llm-cli, which provides a convenient interface for interacting with supported models. Text generation can be done as a one-off based on a prompt, or interactively, through REPL or chat modes. ... -
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T81 558
Applications of Deep Neural Networks
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. -
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smclarify
Fairness aware machine learning. Bias detection and mitigation
Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models. A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The label is a column or feature which is the target for training a machine learning model. -
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sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
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...If you get a prompt of SSH keys being accepted, you are in a good shape to continue. 5. Perform an NMAP scan like how Trinity did to hack the grid! try all ports :) 6. Good luck and enjoy the CTF! Learning Pre-Requisites - This VM does not require exploiting a CVE, or use of MetaSploit/Commercial exploit tools. - Requires intermediary knowlege of linux as it is based on Alpine.
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OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets that are of varying sizes and cover a variety graph machine learning tasks, including prediction of node, link, and graph properties. ... -
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VABlog
YUV/PCM/H264/H265/AAC/FFmpeg/Opengl
VABlog is a full-stack web application project that combines blogging functionality with multimedia and video-related features. It is designed as a learning-oriented system that demonstrates how to build a modern web platform with both frontend and backend components. The project includes user authentication, content publishing, and media management capabilities, allowing users to create and manage posts with embedded video content. It integrates database storage for handling user data, posts, and metadata, ensuring persistence and scalability. ... -
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. -
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UMHDL
Integrated Development Environment (IDE) for learning HDL
UMHDL is an educational Integrated Development Environment (IDE) intended for learning digital designing with programmable logic devices using Hardware Description Languages (HDL) through simulation. It is an open-source application created at the Miguel Hernández University (UMH). The aim for the UMHDL development was to have a graphical application that allows learning the VHDL language without licensing restrictions (using some existing open-source tools) and requiring few resources. ... -
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Guia do Desenvolvedor Back-end
Everything you need to become a back-end developer
...The guide covers Linux, Git, GitHub, HTTP, APIs, programming languages, databases, cloud platforms, Docker, architecture patterns, and related technical areas. It also includes resources for data science, machine learning, artificial intelligence, and scientific Python tools. The repository is organized as a study companion, not as an executable software package. Overall, it is a practical back-end learning reference for planning study paths, exploring technologies, and finding useful external resources. -
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Guia do Desenvolvedor Front-end
Everything you need to become a front-end developer
...Overall, it serves as a curated front-end learning hub rather than a runnable application. -
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Flutter Tutorials
Source code for all the tutorials on FilledStacks' channel
...It emphasizes clean architecture, testability, and scalable code, showing developers how to go beyond toy examples. Tutorials often come with detailed commentary or videos to explain design decisions. The collection helps developers bridge the gap between learning Flutter basics and building production-ready apps. It serves as both a reference and a training resource for those aiming to adopt Flutter in professional projects. -
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e-foto
A free GNU/GPL educational digital photogrammetric workstation
...It supports the utilization of aerial photogrammetric images captured by both analog cameras and digital sensors, enabling professionals to harness diverse data sources for their mapping endeavors. You can access our website at http://www.efoto.eng.uerj.br/ where you will find comprehensive tutorials, thus ensuring a smooth and efficient learning experience. -
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LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
...Its purpose is to show how to align language models with human judgments by training a reward model from human comparisons and then fine-tuning a policy model using that reward signal. The repository includes scripts to train the reward model (learning to rank or score pairs of outputs), and to fine-tune a policy (a language model) with reinforcement learning (or related techniques) guided by that reward model. The code is provided “as is” and explicitly says it may no longer run out-of-the-box due to dependencies or dataset migrations. It was tested on the smallest GPT-2 (124M parameters) under a specific environment (TensorFlow 1.x, specific CUDA / cuDNN combinations). ... -
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texturize
Generate photo-realistic textures based on source images
...It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab. -
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AI Code Translator
Use AI to translate code from one language to another
AI Code Translator is a web-based tool that leverages AI to translate source code from one programming language to another, making cross-language porting or code conversion significantly easier — useful when migrating codebases, experimenting across languages, or learning how code patterns map across languages. The UI is built with Next.js + TypeScript, plus modern tooling like Tailwind CSS, giving a clean frontend experience where you paste or upload code, select target language, and get output quickly. Because it uses AI under the hood, the translation can handle not just syntax but also adapt idioms or patterns appropriately for the target language (though with the usual caveats around AI output correctness). ... -
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Transformers-Interpret
Model explainability that works seamlessly with Hugging Face
Transformers-Interpret is an interpretability tool for Transformer-based NLP models, providing insights into attention mechanisms and feature importance.