Building and Training Generative AI Models

Chapter 1: Introduction to Generative AI Systems.- Chapter 2: Choosing the Right Architecture.- Chapter 3: Data Collection and Preparation.- Chapter 4: Training Fundamentals and Self-Supervised Learning.- Chapter 5: Optimization and Learning Strategies.- Chapter 6: Scaling Training with Infrastructure and Distributed Systems.- Chapter 7: Fine-Tuning and Domain Adaptation.- Chapter 8: Reinforcement Learning with Human Feedback (RLHF).- Chapter 9: Model Compression and Inference Optimization.- Chapter 10: Addressing Bias, Hallucinations, and Failure Modes.- Chapter 11: Evaluation and Benchmarking of Generative Models.- Chapter 12: Future Directions and Deployment in Production.

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• Inteligência artificial
• Aprendizagem mecânica
• Programação e linguagens de script: geral

Generative AI;Deep Learning for Generative Models;Generative Adversarial Networks (GANs) ;Variational Autoencoders (VAEs);Transformer-based AI models;Diffusion models in AI;Training large-scale AI systems