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@@ -11,6 +11,43 @@ Support open source software by participating in [Hacktoberfest](https://hacktob
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> Please check all issues labelled as `hacktoberfest` to start contributing!
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Kindly consider leaving a :star: if you like the repository and our organisation.
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## Project Goals
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### **Purpose**
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- Provide clear, well-documented building blocks for ML (data handling, simple models, utilities) implemented primarily in **Go**, with optional Python access points.
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### **Scope**
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- Small, focused algorithms and demos you can run **locally**.
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- Friendly for first-time contributors: readable code, bite-sized issues, and a straightforward workflow.
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### **Target Audience**
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- Students & self-learners who want to understand “how it works under the hood.”
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- Hacktoberfest / open-source contributors who prefer approachable issues.
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- Practitioners curious about Go in ML pipelines.
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## Key Features & Overview
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-**Go-first implementations**
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The project’s core is in Go (currently ~93% Go). This keeps binaries fast and the code explicit/learnable.
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[View on GitHub → IEEE-VIT/pykitzoid](https://github.com/IEEE-VIT/pykitzoid.git)
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-**Python wrapper (work-in-progress)**
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A thin Python interface (see `pyproject.toml`) is scaffolded so users can experiment from notebooks while the logic stays in Go.
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