Isolation Forests
The go-to algorithm for tabular anomaly detection. It works by isolating anomalies (which are few and different) by randomly selecting a feature and a split value. Anomalies are isolated closer to the root of the tree.
Autoencoders
For high-dimensional data (images, complex sequences), train a neural network to compress and reconstruct normal data. Anomalies will have high reconstruction error. Overkill for simple tabular data.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape