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Free Lessons (4)
Introduction
Course Overview
Neural Network Training and Optimization
Neural Networks Training
Gradient Descent
Transfer Learning
Model Alignment
Model Compression
Fine-Tuning
Synthetic Data Generation
Embeddings and Tokenization
Embeddings
Tokenization Methods
Beam Search
Attention Mechanisms
Multi-Head Self-Attention
Cross-Attention
Flash Attention
Positional Encodings
Masking
Normalization in Transformers
Evaluation Techniques
Perplexity
BLEU and ROUGE
Model Architectures and Comparisons
Choosing Between Different Types of AI Models
Scaling Laws
Elo Rating Systems for LLMs
Diffusion Models
Model Interpretability
Hallucinations and Jailbreaks
How ChatGPT Works?
Learning Techniques
Few-Shot Learning
Chain-of-Thought Prompting
RAG for LLMs
Choosing Between RAG, ICL, and Fine-Tuning In LLMs
Scalability and Efficiency
Mixture of Experts
Vector Databases
Agentic Errors
Wrap Up
Conclusion
Mock interview
Fundamentals of Generative AI
Practice Mock Interview