Search Results for "spreadsheet machine learning" - Page 30
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Showing 2009 open source projects for "spreadsheet machine learning"
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Agent Development Kit (ADK) for Java
An open-source, code-first Java toolkit
...ADK is designed to be flexible and modular so that developers can build simple automation agents or large distributed agent systems depending on their needs. While it integrates well with Google’s AI ecosystem, the framework is designed to remain model-agnostic and compatible with different machine learning platforms. -
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csghub-server
csghub-server is the backend server for CSGHub
csghub-server is the backend component of the CSGHub platform, an open-source infrastructure designed to manage and operate large language models, datasets, and AI development workflows within a private deployment environment. The server acts as a centralized management layer that allows teams to store, organize, and operate AI assets such as models, datasets, and machine learning applications in a manner similar to artifact repositories used in software engineering. Built primarily in the Go programming language, the system enables organizations to run model inference, training, and fine-tuning tasks within a unified platform. It integrates capabilities similar to model repositories like Hugging Face while allowing enterprises to host and manage their AI assets internally for security and compliance purposes. -
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Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
Pixeltable is an open-source Python data infrastructure framework designed to support the development of multimodal AI applications. The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a... -
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trench
Open-Source Analytics Infrastructure
...The platform enables developers to collect events such as page views, user actions, and behavioral metrics while storing them in a column-oriented analytics database optimized for time-series workloads. By combining streaming ingestion with fast analytical queries, the system supports use cases such as product analytics dashboards, observability pipelines, and machine learning data preparation. -
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AI Powered Knowledge Graph Generator
AI Powered Knowledge Graph Generator
AI-Powered Knowledge Graph is an open-source project focused on building knowledge graph systems that integrate artificial intelligence and machine learning to represent complex relationships between data entities. Knowledge graphs organize information as networks of nodes and relationships, allowing applications to analyze connections between concepts, datasets, or real-world entities. By incorporating AI techniques such as natural language processing and semantic reasoning, the project enables systems to automatically extract relationships and insights from large volumes of data. ... -
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The LLM Evaluation guidebook
Sharing both practical insights and theoretical knowledge about LLM
The Evaluation Guidebook is an open educational resource created by Hugging Face that explains how to evaluate machine learning and large language models effectively. It compiles practical insights and theoretical knowledge gathered from real-world evaluation work, including experience managing the Open LLM Leaderboard and designing evaluation tools. The guidebook teaches developers how to design evaluation pipelines, select appropriate metrics, and interpret model performance results. ... -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
xLSTM is an open-source machine learning architecture that reimagines the classic Long Short-Term Memory (LSTM) network for modern large-scale language modeling and sequence processing tasks. The project introduces a new recurrent neural network design that incorporates exponential gating mechanisms and enhanced memory structures to overcome limitations of traditional LSTM models. -
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HuixiangDou
Overcoming Group Chat Scenarios with LLM-based Technical Assistance
...This design allows the system to participate in group discussions without flooding the chat with unnecessary messages. The assistant uses retrieval and ranking methods along with language model reasoning to produce accurate answers for technical topics such as computer vision and machine learning projects. It can be integrated into messaging platforms such as WeChat or other team collaboration tools to assist developer communities. -
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MatMul-Free LM
Implementation for MatMul-free LM
...These optimizations can significantly reduce memory consumption and potentially improve computational efficiency during both training and inference. The repository provides implementations of models at several parameter scales and includes tools for experimenting with the architecture using modern machine learning frameworks. -
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Sparrow
Structured data extraction and instruction calling with ML, LLM
Sparrow is an open-source platform designed to extract structured information from documents, images, and other unstructured data sources using machine learning and large language models. The system focuses on transforming complex documents such as invoices, receipts, forms, and scanned pages into structured formats like JSON that can be processed by downstream applications. It combines several components, including OCR pipelines, vision-language models, and LLM-based reasoning modules to identify and extract meaningful data fields from heterogeneous document layouts. ... -
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nanochat
The best ChatGPT that $100 can buy
nanochat is a from-scratch, end-to-end “mini ChatGPT” that shows the entire path from raw text to a chatty web app in one small, dependency-lean codebase. The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and... -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained inspection and modification after training. ... -
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KServe
Standardized Serverless ML Inference Platform on Kubernetes
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. ... -
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KubeEdge
Kubernetes Native Edge Computing Framework (project under CNCF)
...It consists of a cloud part and an edge part, and provides core infrastructure support for networking, application deployment, and metadata synchronization between the cloud and edge. It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image recognition, event processing, and other high-level applications to the Edge. With business logic running at the Edge, much larger volumes of data can be secured & processed locally where the data is produced. With data processed at the Edge, the responsiveness is increased dramatically and data privacy is protected. -
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Nerves
Craft and deploy bulletproof embedded software in Elixir
Nerves is the open-source platform and infrastructure you need to build, deploy, and securely manage your fleet of IoT devices at speed and scale. Nerves is written in Elixir, but you don’t have to rewrite everything in Elixir to get the advantages of Nerves, simply bring your own code (like C, C++, Python, Rust, and more) and scale up. Nerves use the Erlang runtime system, known for being distributed, fault-tolerant, soft real-time, and highly available. Nerves has the tools you need to... -
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Java Tablesaw
Java dataframe and visualization library
Tablesaw is a dataframe and visualization library that supports loading, cleaning, transforming, filtering, and summarizing data. If you work with data in Java, it may save you time and effort. Tablesaw also supports descriptive statistics and can be used to prepare data for working with machine learning libraries like Smile, Tribuo, H20.ai, DL4J. Import data from RDBMS, Excel, CSV, TSV, JSON, HTML, or Fixed Width text files, whether they are local or remote (http, S3, etc.) Tablesaw supports data visualization by providing a wrapper for the Plot.ly JavaScript plotting library. Here are a few examples of the new library in action. ... -
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AWS Toolkit for JetBrains
A plugin for interacting with AWS from JetBrains IDEs
...If you come across bugs with the toolkit or have feature requests, please raise an issue on our GitHub repository. See the user guide for how to get started, along with what features/services are supported. CodeWhisperer uses machine learning to generate code suggestions from the existing code and comments in your IDE. Supported languages include: Java, Python, and JavaScript. In addition to providing code suggestions within your current file, CodeWhisperer can scan your code package to identify security issues. Connect to AWS using static credentials, credential process, or AWS SSO. ... -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
...The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. It also integrates milestone projects that simulate real-world scenarios, helping users translate abstract concepts into deployable solutions. -
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ARC-AGI
The Abstraction and Reasoning Corpus
...The dataset is structured as grid-based puzzles, where each task requires understanding transformations such as symmetry, counting, or spatial manipulation. Unlike traditional machine learning benchmarks, ARC emphasizes generalization and reasoning over statistical pattern recognition, making it particularly challenging for current AI systems. The repository also includes a browser-based interface that allows humans to attempt solving the tasks manually, providing a baseline for comparison. -
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watercrawl
AI-ready web crawler that extracts and structures website content
WaterCrawl is an open source web crawling and data extraction platform designed to transform website content into structured data suitable for machine learning and AI workflows. It enables developers and researchers to crawl web pages, extract meaningful information, and convert it into formats that are easier to process and analyze. It provides a modern crawling system that can automatically navigate links, control crawl depth, and collect content from targeted sections of a website. ... -
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hCaptcha Challenger
Gracefully face hCaptcha challenge with multimodal llms
hCaptcha Challenger is an open-source automation framework designed to solve hCaptcha verification challenges using computer vision models and multimodal reasoning techniques. The project integrates machine learning models capable of analyzing visual captcha tasks and identifying the correct responses required to pass the verification process. Instead of relying on third-party captcha-solving services or browser scripts, the system operates independently by using pretrained neural networks that can classify images, detect objects, and interpret spatial relationships. ... -
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second-brain-ai-assistant-course
Learn to build your Second Brain AI assistant with LLMs
The Second Brain AI Assistant Course is an open-source educational project designed to teach developers how to build a personal AI assistant that interacts with a user’s knowledge base. The course provides a structured curriculum that walks learners through the architecture and implementation of a production-ready AI system powered by large language models. The concept of a “second brain” refers to a personal knowledge repository containing notes, research, and documents that can be queried... -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution for the development of asynchronous and production-ready services, offering automatic deployment for ML models. -
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AWS IoT FleetWise Edge
AWS IoT FleetWise Edge Agent
...AWS IoT FleetWise makes it easy and cost-effective for automakers to collect, transform, and transfer vehicle data to the cloud in near-real-time and use it to build applications with analytics and machine learning that improve vehicle quality, safety, and autonomy. Train autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) with camera data collected from a fleet of production vehicles. Improve electric vehicle (EV) battery range estimates with crowdsourced environmental data, such as weather and driving conditions, from nearby vehicles. ... -
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POCO
Cross-platform C++ libraries for building network applications
The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++...
