it_portfolio/content/projects/ai_ml.md

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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