Learning Paradigms in Data
Learning paradigms describe how models learn from data. Each paradigm uses a different setup. The goal is to choose the right method based on the problem and the type of data you have.
1. Supervised Learning
Supervised learning uses labeled data. Each input has an output. The model learns the link between them.
Examples
- Spam detection
- Image classification
- Price prediction
2. Unsupervised Learning
Unsupervised learning uses data without labels. The model finds patterns or structure.
Examples
- Clustering
- Dimensionality reduction
- Anomaly detection
3. Semi Supervised Learning
Semi supervised learning uses a mix of labeled and unlabeled data. The model uses labeled data to guide its learning.
Examples
- Text classification with small labeled sets
- Image labeling with limited annotation
4. Self Supervised Learning
Self supervised learning builds labels from the data itself. The model learns from internal signals inside the dataset.
Examples
- Masked word prediction
- Image patch prediction
5. Reinforcement Learning
Reinforcement learning uses interaction. An agent takes actions, gets rewards, and improves its policy.
Examples
- Robotics
- Game playing
- Navigation systems
6. Transfer Learning
Transfer learning takes a model trained on one task and adapts it to another task.
Examples
- Using a pretrained CNN for new image datasets
- Using a pretrained transformer for text tasks
7. Online Learning
Online learning updates the model step by step as new data flows in. It handles streaming data.
Examples
- Real time recommendations
- Live anomaly detection
Learning Paradigms in Moroccan Darija
Learning paradigms hiyya tariq dial t3llam models mn data. Kul paradigm kayst3mel style mokhtalef.
Supervised
Data m3a labels. Model kayt3llam link.
Unsupervised
Data bla labels. Model kayjber patterns.
Semi Supervised
Mxoj dial labeled w unlabeled.
Self Supervised
Model kayt3llam mn data b labels mkhlouqin mn data.
Reinforcement
Agent kaydir actions w kayakhod reward.
Transfer
Training m task o usage f task okhra.
Online
Model kayupdate m3a data jdid.
Conclusion
Learning paradigms offer different ways to train models. Each paradigm serves a specific type of data and task. Understanding them helps you choose the right method for your project.