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Course Outline

Introduction to Generative AI

  • Understanding generative AI and its significance.
  • Overview of primary types and techniques in generative AI.
  • Key challenges and limitations facing generative AI.

Transformer Architecture and LLMs

  • Defining transformers and explaining their operational principles.
  • Exploring the main components and features of transformer models.
  • Leveraging transformers to construct Large Language Models.

Scaling Laws and Optimization

  • Understanding scaling laws and their importance for LLMs.
  • The relationship between scaling laws, model size, data volume, compute resources, and inference needs.
  • Utilizing scaling laws to enhance LLM performance and efficiency.

Training and Fine-Tuning LLMs

  • Primary steps and challenges in training LLMs from the ground up.
  • Advantages and disadvantages of fine-tuning LLMs for specific tasks.
  • Best practices and recommended tools for training and fine-tuning LLMs.

Deploying and Using LLMs

  • Key considerations and challenges in deploying LLMs in production environments.
  • Common use cases and applications of LLMs across various domains and industries.
  • Integrating LLMs with other AI systems and platforms.

Ethics and Future of Generative AI

  • Ethical and social implications of generative AI and LLMs.
  • Potential risks and harms, such as bias, misinformation, and manipulation.
  • Promoting responsible and beneficial use of generative AI and LLMs.

Summary and Next Steps

Requirements

  • A solid understanding of machine learning concepts, including supervised and unsupervised learning, loss functions, and data splitting techniques.
  • Practical experience with Python programming and data manipulation.
  • Foundational knowledge of neural networks and natural language processing.

Target Audience

  • Software developers
  • Machine learning enthusiasts
 21 Hours

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