Metadata-Version: 2.3
Name: auto_mind
Version: 0.2.2
Summary: AutoMind: A Comprehensive Machine Learning Library
Project-URL: Homepage, https://github.com/lucasbasquerotto/auto-mind
Project-URL: Issues, https://github.com/lucasbasquerotto/auto-mind/issues
Author-email: Lucas Basquerotto <lucasbasquerotto@gmail.com>
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# AutoMind: A Comprehensive Machine Learning Library

**AutoMind** is a flexible and extensible Python package designed to streamline the development and deployment of machine learning solutions. At its core, the package features a powerful manager classes that orchestrate various machine learning workflows, allowing users to focus on their specific applications without getting bogged down by implementation details. The package is built to be adaptable, enabling seamless integration of custom algorithms and models.

### Key Features:
- **Supervised Learning Management**: Effortlessly handle the training process for models with labeled datasets. While basic algorithms are provided, the focus is on managing and optimizing the workflow.
- **Reinforcement Learning Orchestration** *(to be done)*: A robust framework for managing RL environments and training processes, making it easy to experiment and deploy RL agents.
- **Semantic Processing Coordination** *(to be done)*: Tools for handling the end-to-end process of vectorizing meanings, processing them through neural architectures, and decoding them into useful formats.

Whether you're building traditional models, exploring reinforcement learning, or working with complex semantic vectors, **AutoMind** provides the infrastructure to manage your projects efficiently while allowing room for customization and expansion.

## Explore AutoMind Examples

To see **AutoMind** in action, explore our dedicated repository for examples and tutorials: [auto-mind-examples](https://github.com/lucasbasquerotto/auto-mind-examples).

This repository contains a variety of examples, including:

- **Supervised Learning**: Learn how to manage and train models using labeled datasets.
- **Reinforcement Learning** *(to be done)*: Set up RL environments, train agents, and analyze their performance.
- **Semantic Processing** *(to be done)*: Work with vectorized meanings and process semantic information.

Whether you're getting started or looking to expand your understanding of the **AutoMind** package, these examples will provide valuable insights and practical guidance.
