AI
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- Layers of AI
Trace the conceptual hierarchy from artificial intelligence and machine learning to deep learning, language models, and AI engineering.
- Language Models (LM)
Explore probabilistic language modeling, tokens and vocabularies, and the difference between masked and autoregressive models.
- Large Language Models (LLM)
Follow how large language models are built, from data preparation and architecture to training, inference, scaling, and optimization.
- Foundation Models
Understand foundation models and survey representative models for text, images, speech, and other modalities.
- AI Engineering
Examine why AI engineering emerged, how it differs from conventional software development, and what its application stack involves.
- Traditional Software Development
Review the conventional software delivery lifecycle, from requirements and research to implementation, testing, performance, and operations.
- Retrieval-Augmented Generation (RAG)
A structured guide to retrieval-augmented generation: motivation, indexing, retrieval, context assembly, optimization, and evaluation.
- AI Agents
Understand AI agents through tool use, planning, the ReAct pattern, and the challenges of reliable multi-step execution.
- Fine-Tuning
Examine when fine-tuning helps, how it compares with prompting and RAG, and how to train, optimize, and evaluate adapted models.
- Prompt Engineering
Explore prompt structure, context, examples, constraints, security risks, and practical ways to improve model instructions.