AI & Machine Learning Glossary (Quick Overview)
This glossary explains common AI and ML terms. Below are a few examples. For the full version, use the interactive glossary below where you can search, filter by level, and browse by category. Click any related term to jump to its definition.
Here are some of the key terms covered in this glossary:
Gradient Descent, Learning Rate, Overfitting, Regularization, Cross-Validation, Precision & Recall, ROC-AUC, Convolution, Data Augmentation, Transformer, Tokenization, Epoch, Activation Function, CNN, ReLU, Loss Function, Clustering, Feature Scaling, Transfer Learning, Fine-Tuning, Reinforcement Learning, GAN, Random Forest, SVM, Bayesian Optimization
- All Levels
- Beginner
- Intermediate
- Advanced
- All Categories
- Optimization
- General
- Evaluation
- Computer Vision
- NLP
- Reinforcement Learning
- Deep Learning
- Data Science
- Time Series
- Anomaly Detection
- Recommender Systems
- Natural Language Generation
- Speech Recognition
- Ethics in AI
- Model Deployment
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Conclusion
This glossary is meant to be a practical reference as you explore the world of Artificial Intelligence and Machine Learning. The field is growing fast, with new terms and concepts appearing every year. Use this guide as a starting point, and revisit it whenever you come across unfamiliar jargon. As AI continues to evolve, staying familiar with the language of the field will help you understand ideas more clearly and connect them to real-world applications.
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