Metadata-Version: 2.4
Name: skleaarn
Version: 1.0.0
Summary: ML competition problem solutions and study notes collection
Author: David
License: MIT
Keywords: machine-learning,sklearn,competition,notes,pytorch
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# skleaarn

A personal ML problems collection and learning notes package. Installing this package automatically copies all ML competition solutions and study notes to your Desktop.

## Installation

```bash
pip install skleaarn
```

On first import, the package will automatically install the `Sklearn` folder to your Desktop (or `~/Documents/Sklearn` as fallback). It also creates a Start Menu shortcut on Windows.

## Manual Install

```python
import skleaarn
skleaarn.install()                     # installs to ~/Desktop/Sklearn
skleaarn.install("C:/MyFolder")       # installs to a custom location
```

Or via command line:

```bash
skleaarn                               # installs to ~/Desktop/Sklearn
skleaarn C:/MyFolder                  # installs to a custom location
```

## What's Included

### Problems/
58 Python files with ML competition solutions, covering:
- **Computer Vision**: UNet segmentation, ResNet classification, GhostHunting, HotSpot, Skeletons, EmojiSegmentation, Broken image tasks, GlitchHunter
- **NLP / Text**: ToxicOnline (BiLSTM + FastText + Word2Vec), HieroglyphHunter (GloVe), OmVsAI, SavingChristmas
- **Tabular ML**: HeartBeat, RaspberryPicking, ANPC, CalitateaSolului, Churn, Muzee, FaultyLLM
- **Reinforcement Learning**: RL Value Iteration, Q-Learning (LandoNorris, MountainCar), Drone RL, Labirint RL, StochasticRIFT
- **Audio**: AICC3 (sound classification), DigitTags

### LearnIt_Notes/
- **NOTES.md** — 31 categorized theory notes covering: general PyTorch patterns, ResNet, LSTM/BiLSTM, NLP (GloVe, Word2Index, Padding), UNet, Computer Vision tricks, Audio (spectrograms), important gotchas
- **snippets/** — 5 ready-to-use code templates:
  - `01_Scheduler_When_and_What.py`
  - `02_F1_Train_Val_loop.py`
  - `03_UNET.py`
  - `04_Double_Conv_for_UNET.py`
  - `05_Train_Val_split.py`

## Categories Covered

| Category | Description |
|---|---|
| `general` | PyTorch patterns, loss functions, model saving |
| `resnet` | ResNet fine-tuning, embeddings, unfreezing layers |
| `lstm` | BiLSTM, multi-label, Word2Index |
| `nlp` | GloVe, sentence embeddings, padding, NLTK |
| `vision` | UNet, ConnectedComponents, image preprocessing |
| `audio` | Spectrograms, audio segmentation |
| `unet` | UNet getitem, prediction loops |
| `important` | Critical reminders (zero_grad, getting predictions) |
| `output` | Cosine similarity for retrieval |

## Version

1.0.0 — Initial release
