AI Models are not a monolith. From the tiny decision trees in your calculator to the massive LLMs running in global data centers, the architecture defines the capability.
1. Learning Paradigms
Supervised Learning
The 'Teacher' model. Trained on labeled data.
- Key Models: Linear/Logistic Regression, Random Forests, XGBoost.
- Objective: Map X to Y with minimum error.
Unsupervised Learning
The 'Self-Discovery' model. Finds hidden patterns in unlabeled data.
- Key Models: K-Means Clustering, PCA (Principal Component Analysis).
- Objective: Find the structure of X.
Reinforcement Learning (RL)
The 'Reward' model. Learns by trial and error in an environment.
- Key Models: PPO (Proximal Policy Optimization), Q-Learning.
- Objective: Maximize cumulative reward.
2. Structural Architectures
Transformers (The Current King)
The backbone of modern AI (GPT, BERT, Claude). They use an Attention Mechanism to understand context across long sequences of data.
Convolutional Neural Networks (CNN)
The 'Eyes' of AI. Designed to process grid-like data (Images). They use filters to detect edges, textures, and eventually objects.
Diffusion Models
The 'Artists'. They learn to generate data by reversing a noise process. This is what powers DALL-E and Midjourney.
3. Deployment Classification
- Edge AI: Tiny models optimized for mobile and IoT (TensorFlow Lite).
- Cloud Foundation Models: Trillion-parameter giants (GPT-4o).
- Specialized Small Language Models (SLMs): Domain-specific models like BioBERT or CodeLlama.
The choice of model is always a balance between accuracy, latency, and cost.
