Search Results for "ekho-data" - Page 22
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Showing 3593 open source projects for "ekho-data"
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skfolio
Python library for portfolio optimization built on top of scikit-learn
skfolio is a Python library designed for portfolio optimization and financial risk management that integrates closely with the scikit-learn ecosystem. The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. It supports a wide range of allocation methods, from classical mean-variance optimization to modern techniques that rely on clustering, factor models, and risk-based allocations. ... -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. Machine learning models are then trained to estimate the probability that a team will win a game as well as whether the total score will fall above or below the sportsbook’s predicted total. ... -
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Book1_Python-For-Beginners
The Iris Book: Addition, Subtraction, Multiplication, and Division
...It integrates visual aids and annotated code examples to help learners understand not just how Python works but why certain patterns are used. The material is structured to support self-paced learning, making it suitable for students, career switchers, and hobbyists. Because the book is part of a larger data science pathway, it also prepares readers for later work in visualization and machine learning. Overall, it serves as an accessible on-ramp into Python within a broader analytical learning journey. -
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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The content is organized progressively so learners can build confidence before moving into more advanced quantitative subjects. ... -
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OpenPlanter
Language-model investigation agent with a terminal UI
...With its modular Python codebase, users can adapt the platform for different plant types, hardware setups, or automation strategies. Overall, OpenPlanter aims to simplify the creation of programmable, data-driven plant care systems. -
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BettaFish
Public opinion analysis system
BettaFish is an open-source, multi-agent public opinion analysis system built to automate the collection, deep analysis, and reporting of social media data at scale through conversational queries. It uses a modular architecture of specialized agents that collaborate to crawl mainstream platforms, extract multimodal content like text and short video, and synthesize insights through both statistical and large language model techniques. With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. ... -
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Tree
tree is a library for working with nested data structures
Tree (dm-tree) is a lightweight Python library developed by Google DeepMind for manipulating nested data structures (also called pytrees). It generalizes Python’s built-in map function to operate over arbitrarily nested collections — including lists, tuples, dicts, and custom container types — while preserving their structure. This makes it particularly useful in machine learning pipelines and JAX-based workflows, where complex parameter trees or hierarchical state representations are common. ... -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. ... -
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Skyvern
Automate browser-based workflows with LLMs and Computer Vision
...Skyvern understands how to solve CAPTCHAs to complete complicated workflows. Support for authenticating into user accounts, including support for 2FA/TOTP. Extract data from workflows in any schema of your choice including CSV or JSON. Automate procurement pipelines, breeze through government forms, and complete workflows in any language. -
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Python JSONPath Next-Generation
JSONPath implementation for Python that aims to be standard compliant
A final implementation of JSONPath for Python that aims to be standard compliant, including arithmetic and binary comparison operators, as defined in the original JSONPath proposal. This package merges both jsonpath-rw and jsonpath-rw-ext and provides several AST API enhancements, such as the ability to update or remove nodes in the tree. This library provides a robust and significantly extended implementation of JSONPath for Python. It is tested with CPython 3.7 and higher. This library... -
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Elasticsearch MCP Server
A Model Context Protocol (MCP) server implementation
This MCP server implementation provides interaction capabilities with Elasticsearch and OpenSearch, enabling functionalities such as document searching, index analysis, and cluster management through a set of tools. -
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Groq Python
The official Python Library for the Groq API
...Through this library, you can call Groq’s models from Python code — for example to request chat completions, code generation, transcription, or any supported endpoint — using idiomatic Python syntax. The SDK handles authentication (via environment variable or parameter), defines proper type-safe request/response data types, and supports both synchronous and asynchronous usage patterns depending on your application needs. This makes it easy to integrate Groq-powered AI capabilities into backend services, data pipelines, research notebooks, or applications written in Python. For those building AI-based tooling, automation scripts, or ML-backed backends, groq-python abstracts away HTTP request plumbing and exposes a clean API, accelerating development and reducing boilerplate. -
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LLMStack
No-code multi-agent framework to build LLM Agents, workflows
LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. Trigger your AI chains from Slack or Discord. -
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Lightly
A python library for self-supervised learning on images
...We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. ... -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). -
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Flowly AI
Flowly is 100x faster than OpenClaw
Flowly is an open-source personal AI assistant that runs locally on your machine and connects to multiple communication platforms like Telegram, WhatsApp, Discord, and Slack. It acts as a centralized AI system that can perform tasks such as web browsing, file management, command execution, scheduling, and more—all while keeping your data private. Designed for flexibility, Flowly supports multiple AI providers and models through LiteLLM, allowing users to customize how their assistant behaves. It features a multi-agent architecture where different specialized agents can collaborate, delegate tasks, and operate in parallel. Flowly also includes voice capabilities, enabling real-time phone interactions using speech-to-text and text-to-speech systems. ... -
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FL4Health
Library to facilitate federated learning research
FL4Health is a Vector Institute toolkit for building modular, clinically-focused FL pipelines. Tailored for healthcare, it supports privacy-preserving FL, heterogeneous data settings, integrated reporting, and clear API design. -
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Griptape
Python framework for AI workflows and pipelines with chain of thought
...For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition between those two dimensions effortlessly based on their use case. Griptape not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. ... -
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doccano
Open source annotation tool for machine learning practitioners
doccano is an open-source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence-to-sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours. -
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DBOS Transact PY
Lightweight Durable Python Workflows
...It simplifies building resilient and idempotent applications by enabling Python functions to retain their state, restart after failure, and guarantee consistency. It's designed for data-heavy and fault-intolerant use cases. -
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Heretic
Fully automatic censorship removal for language models
...The project can decensor many popular dense and some mixture-of-experts (MoE) models, supporting workflows that would otherwise require manual tuning. Beyond simple decensoring, Heretic includes research-oriented options for analyzing model internals and interpretability data. -
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Tally
Let agents classify your bank transactions
Tally is an open-source, AI-assisted tool designed to automate the classification of personal financial transactions, helping users turn raw bank data into meaningful categories without manual tagging. At its core, Tally pairs a local rule engine with large language models so that an AI assistant (like Claude Code, Copilot, or any CLI agent) interprets, suggests, and categorizes expenses, savings, subscriptions, and income events based on your own rules and behavior. It generates human-readable reports and can produce HTML, JSON, or Markdown outputs to suit dashboards or personal finance workflows. ... -
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sqlite-utils
Python CLI utility and library for manipulating SQLite databases
...It focuses on making common tasks like importing CSV/JSON, exploring tables, and running ad-hoc queries feel ergonomic and scriptable. As a CLI, it lets you build databases from structured data in one line, run queries against local files or in-memory databases, output results as JSON, CSV, or pretty tables, and configure full-text search. As a library, it exposes high-level APIs for inserting records, creating or transforming tables, normalizing schemas, and running migrations that SQLite’s limited ALTER TABLE cannot handle directly. ... -
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RedAmon
AI-powered framework for automated penetration testing and red teaming
...It begins with a multi-phase reconnaissance engine that maps the entire attack surface of a target, collecting information such as subdomains, open ports, services, and potential vulnerabilities. RedAmon then uses an AI agent orchestrator to analyze this data, select appropriate tools, and perform exploitation steps such as credential brute forcing or CVE-based attacks. All discovered assets, relationships, and vulnerabilities are stored in a Neo4j knowledge graph, allowing the system to reason about the environment and make informed decisions during the attack process. -
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Solace Agent Mesh
An event-driven framework designed to build multi-agent AI systems
Solace Agent Mesh is an event-driven framework designed to build, orchestrate, and scale multi-agent AI systems where specialized agents collaborate to solve complex tasks across distributed environments. It addresses one of the main challenges in modern AI systems, which is connecting isolated agents, data sources, and enterprise systems into a cohesive and interoperable ecosystem. The framework uses an asynchronous messaging architecture powered by an event broker, enabling agents to communicate reliably without tight coupling, which significantly improves scalability and fault tolerance. It introduces a standardized agent-to-agent communication protocol that allows different agents, regardless of their implementation or location, to exchange tasks, share data, and coordinate workflows efficiently. ...