refacto: vide-coding to get a terminal look

content/projects/ai_ml.md

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+# AI & Machine Learning Projects
+
+Experiments at the boundary of classical algorithms and modern deep learning.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+*Press Q or ESC to return.*