Large Language Models

Introduction to Large Language Models

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History and Evolution of Neural Language Models

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Fundamentals of Natural Language Processing

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Understanding Transformer Architecture

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Attention Mechanisms Explained

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Word Embeddings and Vector Representations

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Tokenization and Preprocessing Techniques

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Training Data and Dataset Considerations

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Loss Functions and Optimization in LLMs

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Foundation Concepts Assessment

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Transfer Learning and Pre-training Strategies

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Fine-tuning Language Models for Specific Tasks

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Prompt Engineering Fundamentals

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Few-shot and Zero-shot Learning

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Chain of Thought Prompting Techniques

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Model Evaluation Metrics and Benchmarks

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Scaling Laws and Model Size Trade-offs

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Core Skills Practice Exercise

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Inference Optimization and Quantization

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Building LLM-powered Applications Project

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API Integration and Deployment

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Safety, Bias, and Ethical Considerations

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Hallucinations and Reliability Issues

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Retrieval-Augmented Generation (RAG)

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Advanced Skills Consolidation Exercise

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Multi-modal Language Models

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Parameter-Efficient Fine-tuning Methods

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Mixture of Experts and Sparse Models

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Alignment and Reinforcement Learning from Human Feedback

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Advanced Topics Application Exercise

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Emerging Architectures and Future Directions

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Efficient Inference at Scale

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LLM Interpretability and Analysis

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Building Production-Ready LLM Systems

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Capstone: Advanced LLM Development Project

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