Search Results for "learning" - Page 53
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Showing 4736 open source projects for "learning"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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GH Archive
GH Archive is a project to record the public GitHub timeline
...The dataset is also published through Google BigQuery for large-scale SQL-style exploration without downloading every archive. Its structure supports trend analysis, visualizations, machine learning, and open-source ecosystem research. The repository contains the crawler, supporting scripts, and website code, while the actual event files are hosted separately. -
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SIA
AI framework to autonomously improve the performance of any AI system
...The framework can refine both the harness around the task and the agent implementation itself. It is aimed at research and experimentation across tasks such as machine learning benchmarks, legal classification, code optimization, and scientific workflows. It includes built-in tasks, a command-line runner, and a visual dashboard for following generations as they evolve. It also lets users define custom providers, profiles, seed agents, and task directories without changing the core code. -
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kagglehub
Python library to access Kaggle resources
...The library is designed to work both inside and outside Kaggle Notebooks, with native behavior that can adapt when it runs in Kaggle’s hosted notebook environment. It is useful for machine learning workflows where data, models, and notebook artifacts need to be pulled into scripts, experiments, or pipelines. kagglehub also supports authentication so users can access private or restricted resources when their account has permission. Its main value is making Kaggle assets easier to consume programmatically in Python-first data science and AI development workflows. -
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Cheat on Content
Workflow that turns every post into a calibrated experiment
...The project is built around the loop of score, predict, publish, retro, and improve. It is aimed at creators, marketers, and operators who want to build a repeatable system for learning from every published piece. Its value is strongest for people who already create consistently but need a better way to extract insight from their output. -
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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How-To-Ask-Questions-The-Smart-Way
Correctly propose technical questions and get the answers you want
...It also highlights the importance of respecting the time and expertise of others, encouraging a culture of constructive collaboration. The guide is particularly valuable for beginners who are learning how to interact in open-source communities and forums. By improving communication, it increases the likelihood of receiving helpful and accurate responses. Overall, it serves as a foundational resource for effective problem-solving and collaboration in software development. -
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Keychron Hardware Design
Industrial design files for Keychron keyboards and mice
...It is designed both as an educational resource and as a foundation for creating compatible accessories, giving users the ability to measure, remix, and prototype hardware components. The project encourages experimentation and learning by providing structured documentation, guides, and model organization across product families. -
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LaiNES
Compact cycle-accurate NES emulator
...It also provides savestate functionality that captures the full emulator state, including CPU, graphics, and audio subsystems, allowing users to resume gameplay seamlessly. LaiNES strikes a balance between educational clarity and functional completeness, making it both a learning tool and a usable emulator. -
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anti-distill
Anti-distillation for employee Skills
anti-distill is a research-oriented project focused on protecting machine learning models from knowledge distillation attacks, where smaller models attempt to replicate the behavior of larger proprietary systems. The project explores techniques that make it harder for external models to learn from outputs, thereby preserving intellectual property and model uniqueness. It likely introduces methods such as output perturbation, watermarking, or response shaping to prevent accurate imitation. ... -
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AI Agent Deep Dive
AI Agent Source Code Deep Research Report
...It breaks down complex concepts such as planning, tool usage, memory management, and multi-step reasoning into digestible explanations and practical examples. The project is organized as a learning resource rather than a standalone framework, making it particularly useful for developers who want to move beyond surface-level prompt engineering into full agent system design. It explores how agents interact with environments, execute tasks, and maintain context over time, highlighting both strengths and limitations of current approaches. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. ... -
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The AI Scientist-v2
Workshop-Level Automated Scientific Discovery via Agentic Tree Search
...The platform is capable of generating original research ideas, designing and executing experiments, analyzing and visualizing results, and producing full academic papers without direct human intervention. It introduces a generalized framework that removes reliance on predefined templates, enabling broader applicability across multiple machine learning domains and more open-ended exploration of research problems. A key innovation is its progressive agentic tree search, which systematically explores experimental paths and is coordinated by an experiment manager agent that guides decision-making. The system also integrates automated review mechanisms, including vision-language feedback loops, to iteratively refine the quality of generated research outputs. -
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Numbast
Build an automated pipeline that converts CUDA APIs into Numba
...This approach significantly improves developer productivity by reducing boilerplate code and ensuring consistency between C++ and Python interfaces. Numbast is particularly useful for teams working with custom CUDA libraries or extending existing ones into Python ecosystems for data science and machine learning. It complements tools like Numba, which compile Python code into GPU-executable kernels, by expanding the range of accessible CUDA functionality. -
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JAX Toolbox
Public CI, Docker images for popular JAX libraries
JAX Toolbox is a development toolkit designed to streamline and optimize the use of JAX for machine learning and high-performance computing on NVIDIA GPUs. It provides prebuilt Docker images, continuous integration pipelines, and optimized example implementations that help developers quickly set up and run JAX workloads without complex configuration. The project supports popular JAX-based frameworks and models, including architectures used for large-scale pretraining such as GPT and LLaMA variants. ... -
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Google Kubernetes Engine (GKE) Samples
Sample applications for Google Kubernetes Engine (GKE)
...It serves as a practical companion to official GKE tutorials, providing real, runnable code that illustrates how containerized applications are packaged, deployed, and scaled within Kubernetes clusters. The repository is organized into multiple categories such as AI and machine learning, autoscaling, networking, observability, security, and cost optimization, allowing developers to explore specific use cases and architectural patterns. It includes both simple quickstart examples, like basic “hello world” applications, and more advanced scenarios such as migrating monolithic applications to microservices, implementing service meshes, and configuring custom autoscaling metrics. -
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MetaScreener
AI-powered tool for efficient abstract and PDF screening
...The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with classification and prioritization of candidate papers. The platform can analyze both abstracts and full PDF documents, enabling automated filtering based on research criteria defined by the user. By incorporating natural language processing techniques, the system can identify potentially relevant studies and reduce the workload associated with manual screening. -
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NLP-Knowledge-Graph
Research and application of technologies such as nl processing
NLP-Knowledge-Graph is an open educational repository that collects resources, research materials, and tutorials focused on the intersection of natural language processing and knowledge graph technologies. The project aims to help researchers and developers understand how structured knowledge representations can enhance language processing systems. It includes curated materials covering key topics such as knowledge graph construction, entity recognition, relation extraction, graph... -
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LLM-Finetuning
LLM Finetuning with peft
LLM-Finetuning is an open educational repository that provides practical notebooks and tutorials for fine-tuning large language models using modern machine learning frameworks. The project focuses on parameter-efficient fine-tuning methods such as LoRA and QLoRA, which allow large models to be adapted to new tasks without requiring full retraining. Instead of requiring specialized hardware or complex training pipelines, many examples are designed to run in cloud notebook environments such as Google Colab. ... -
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Open Deep Research
An AI-powered research assistant that performs iterative research
...The system exposes parameters such as breadth and depth to control how widely and how deeply the agent explores information sources. It is intentionally kept compact, with a codebase under roughly 500 lines, making it highly approachable for experimentation and learning. The architecture demonstrates how modern agent pipelines can continuously gather evidence, extract learnings, and adjust research direction over time. -
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Qwen3-VL-Embedding
Multimodal embedding and reranking models built on Qwen3-VL
Qwen3-VL-Embedding (with its companion Qwen3-VL-Reranker) is a state-of-the-art multimodal embedding and reranking model suite built on the open-sourced Qwen3-VL foundation, developed to handle diverse inputs including text, images, screenshots, and videos. The core embedding model maps such inputs into semantically rich vectors in a unified representation space, enabling similarity search, clustering, and cross-modal retrieval. The reranking model then precisely scores relevance between a... -
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Go Katas
A collection of daily coding challenges
Go Katas is a curated collection of practice exercises and coding challenges specifically crafted to improve proficiency in Go, including idiomatic patterns, language fundamentals, and algorithm design. It mirrors the kata practice tradition from martial arts—repetitive, thoughtful practice where each exercise reinforces technique, discipline, and problem-solving approach. Each kata prompt focuses on a precise aspect of Go, such as concurrency patterns, memory management, interfaces, error... -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
...A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment. -
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Flight rules for Git
Flight rules for git
...Each rule is written in a concise, example-oriented style so users can quickly understand what to do and why it matters when they encounter a specific situation. The repository serves as both a learning tool and a quick lookup for urgent scenarios, making Git less intimidating and more predictable. -
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MiniMind-V
"Big Model" trains a visual multimodal VLM with 26M parameters
...MiniMind-V combines techniques from modern vision-language modeling but focuses on efficiency and simplicity so that individuals or small teams can explore multimodal learning without massive GPU clusters. It includes training scripts, model definitions, and associated tooling that illustrate how to build and evaluate such lightweight models. While not intended to compete with large production models, it serves as a hands-on educational resource and starting point for experimentation. -
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Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
...Datumaro makes it easy to merge datasets, split them into training/validation/test subsets, filter or transform annotations, and validate annotation quality — all while preserving metadata and supporting detailed statistics. It’s especially useful when you’re dealing with heterogeneous data sources or need to prepare complex datasets for machine learning workflows, freeing you from writing custom scripts for every format conversion. -
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Rust Latam
Learn to write Rust procedural macros
...The repo contains multiple toy/realistic macro projects drawn from real use-cases: e.g., derive(Builder), derive(CustomDebug), seq!, #[sorted], #[bitfield]. The README indicates the focus is on learning: parsing token streams, generating code, handling generics, attribute arguments, etc. It has test harness and workflow guidance. Because procedural macros are quite subtle in Rust, this workshop is a strong resource for anyone wanting to go from beginner to intermediate/advanced macro writing.