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Generative AI Tutorial Topics
Basics
Introduction to Generative AI
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What is Generative AI?
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History and evolution of Generative AI
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Key concepts and terminology
Fundamentals of Machine Learning
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Basic principles of machine learning
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Supervised vs. unsupervised learning
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Common algorithms and models
Basic Neural Networks
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Introduction to neural networks
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Structure of a neural network
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Training and evaluating neural networks
Intermediate
Deep Learning Techniques
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Introduction to deep learning
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Convolutional neural networks (CNNs)
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Generative adversarial networks (GANs)
Natural Language Processing
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Basics of NLP
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Language models and embeddings
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Transformer models
Data Handling and Preparation
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Data collection and preprocessing
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Data augmentation techniques
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Ethical considerations in data handling
Advanced
Advanced Generative Techniques
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Advanced GAN techniques
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State-of-the-art transformer models
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Hybrid models and architectures
Optimization and Performance
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Model optimization techniques
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Efficient training methods
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Performance evaluation and benchmarking
Future Directions
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Emerging trends in generative AI
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Ethics and societal impact
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Future research directions