AI Concepts Developers Should Learn

1. Machine Learning: Core algorithms, statistics, and model training techniques.
2. Deep Learning: Neural networks with multiple layers that learn complex patterns automatically.
3. Neural Networks: Layered structures that model complex, nonlinear relationships in data.
4. NLP: Techniques for processing and understanding human language in text form.
5. Computer Vision: Algorithms that interpret and analyze images and visual data.
6. Reinforcement Learning: Training agents through rewards and penalties in interactive environments.
7. Generative Models: AI systems that create new data based on learned patterns.
8. LLM: Large language models that generate human-like text from training data.
9. Transformers: Self-attention architecture that powers most modern AI language models.
10. Feature Engineering: Selecting and designing input features to improve model performance.
11. Unsupervised Learning: Finding patterns in data without using labeled examples.
12. Bayesian Learning: Using probabilistic methods to incorporate uncertainty into models.
13. Prompt Engineering: Crafting effective inputs to guide AI model outputs optimally.
14. AI Agents: Autonomous systems that observe environments and take intelligent actions.
15. Fine-Tuning Models: Adapting pre-trained models for specific tasks and domains.
16. Multimodal Models: AI systems that process text, images, audio, and video together.
17. Embeddings: Converting data into numerical vectors that machines can process effectively.
18. Vector Search: Finding similar items by comparing their mathematical vector representations.
19. Model Evaluation: Testing and measuring how well AI models perform on tasks.
20. AI Infrastructure: Building scalable systems to deploy and run AI applications.