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AI Concepts Every Developer Should Know
- metadat 1y ago- Machine Learning: Core algorithms, statistics, and model training techniques. - Deep Learning: Hierarchical neural networks learning complex representations automatically. - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately. - NLP: Techniques to process and understand natural language text. - Computer Vision: Algorithms interpreting and analyzing visual data effectively - Reinforcement Learning: Distributed traffic across multiple servers for reliability. - Generative Models: Creating new data samples using learned data. - LLM: Generates human-like text using massive pre-trained data. - Transformers: Self-attention-based architecture powering modern AI models. - Feature Engineering: Designing informative features to improve model performance significantly. - Supervised Learning: Learns useful representations without labeled data. - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches. - Prompt Engineering: Crafting effective inputs to guide generative model outputs. - AI Agents: Autonomous systems that perceive, decide, and act. - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks. - Multimodal Models: Processes and generates across multiple data types like images, videos, and text. - Embeddings: Transforms input into machine-readable vector formats. - Vector Search: Finds similar items using dense vector embeddings. - Model Evaluation: Assessing predictive performance using validation techniques. - AI Infrastructure: Deploying scalable systems to support AI operations. Are there any other AI concepts you would add to the list?