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All LessonsFree Lessons (4)
Introduction
Course Overview
Neural Network Training and Optimization
Neural Networks TrainingGradient DescentTransfer LearningModel AlignmentModel CompressionFine-TuningSynthetic Data Generation
Embeddings and Tokenization
EmbeddingsTokenization MethodsBeam Search
Attention Mechanisms
Multi-Head Self-AttentionCross-AttentionFlash AttentionPositional EncodingsMaskingNormalization in Transformers
Evaluation Techniques
PerplexityBLEU and ROUGE
Model Architectures and Comparisons
Choosing Between Different Types of AI ModelsScaling LawsElo Rating Systems for LLMsDiffusion ModelsModel InterpretabilityHallucinations and JailbreaksHow ChatGPT Works?
Learning Techniques
Few-Shot LearningChain-of-Thought PromptingRAG for LLMsChoosing Between RAG, ICL, and Fine-Tuning In LLMs
Scalability and Efficiency
Mixture of ExpertsVector DatabasesAgentic Errors
Wrap Up
Conclusion
Mock interview
Fundamentals of Generative AI
Practice Mock Interview