# AI & Machine Learning Projects

Experiments at the boundary of classical algorithms and modern deep learning.

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## Handwritten Digit Classifier

A convolutional neural network trained on MNIST, built from scratch using **PyTorch** without relying on pretrained weights.

**Architecture:**

```
Input (1×28×28)
  → Conv2d(1, 32, 3) + ReLU + MaxPool
  → Conv2d(32, 64, 3) + ReLU + MaxPool
  → Dropout(0.25)
  → Linear(64×5×5 → 128) + ReLU
  → Dropout(0.5)
  → Linear(128 → 10)
  → LogSoftmax
```

**Results:** 99.2 % test accuracy after 10 epochs on a single CPU.

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## Sentence Similarity Engine

A small semantic search tool that encodes sentences into embeddings and retrieves the most similar entries from a knowledge base.

**Approach:**

- Sentence embeddings via a fine-tuned BERT variant (`sentence-transformers`)
- FAISS index for approximate nearest-neighbour search at scale
- CLI interface — `search.py "your query"`

**Use case:** powering a private personal knowledge base search over Markdown notes.

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## Reinforcement Learning: Grid World

A from-scratch implementation of Q-Learning and SARSA applied to a configurable grid-world environment.

**Implemented:**

- Tabular Q-learning with ε-greedy exploration
- SARSA (on-policy variant)
- Policy iteration and value iteration for comparison
- Visualiser showing the learned value function as a heatmap

**Written in pure Python + NumPy** — no RL libraries — for learning purposes.

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## Anomaly Detection on Time Series

A pipeline for detecting anomalies in server metric data (CPU, memory, latency).

**Methods compared:**

- Z-score baseline
- Isolation Forest
- LSTM autoencoder (reconstruction error threshold)

**Outcome:** LSTM autoencoder outperformed statistical methods by ~18 % precision on labelled incidents from a personal homelab dataset.

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