Get in Touch

Course Outline

Introduction to Generative AI

  • Definition and significance of generative AI.
  • Primary types and techniques used in generative AI.
  • Key challenges and limitations associated with generative AI.

Transformer Architecture and LLMs

  • Understanding the transformer model and its functioning.
  • Core components and features of transformers.
  • Utilizing transformers to construct Large Language Models.

Scaling Laws and Optimization

  • Definition and importance of scaling laws for LLMs.
  • The relationship between scaling laws, model size, data volume, compute budget, and inference requirements.
  • Strategies for using scaling laws to enhance LLM performance and efficiency.

Training and Fine-Tuning LLMs

  • Primary steps and challenges involved in training LLMs from scratch.
  • Advantages and disadvantages of fine-tuning LLMs for specific tasks.
  • Best practices and tools for training and fine-tuning LLMs.

Deploying and Using LLMs

  • Key considerations and challenges of 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.
  • Responsible and beneficial applications of generative AI and LLMs.

Summary and Next Steps

Requirements

  • A solid understanding of machine learning principles, including supervised and unsupervised learning, loss functions, and data splitting techniques.
  • Proficiency in Python programming and data manipulation.
  • Fundamental knowledge of neural networks and natural language processing.

Target Audience

  • Software Developers
  • Machine Learning Enthusiasts
 21 Hours

Number of participants


Price per participant

Testimonials (7)

Upcoming Courses

Related Categories