Course Code: generativeaillm
Duration: 21 hours
Prerequisites:
  • An understanding of machine learning concepts, such as supervised and unsupervised learning, loss functions, and data splitting
  • Experience with Python programming and data manipulation
  • Basic knowledge of neural networks and natural language processing

Audience

  • Developers
  • Machine learning enthusiasts
Overview:

Generative AI is a type of AI that can create original content such as text, images, music, and code. Large language models (LLMs) are powerful neural networks that can process and generate natural language. 

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

  • Explain what generative AI is and how it works.
  • Describe the transformer architecture that powers LLMs.
  • Use empirical scaling laws to optimize LLMs for different tasks and constraints.
  • Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
  • Discuss the opportunities and risks of generative AI for society and business.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Generative AI

  • What is generative AI and why is it important?
  • Main types and techniques of generative AI
  • Key challenges and limitations of generative AI

Transformer Architecture and LLMs

  • What is a transformer and how does it work?
  • Main components and features of a transformer
  • Using transformers to build LLMs

Scaling Laws and Optimization

  • What are scaling laws and why are they important for LLMs?
  • How do scaling laws relate to the model size, data size, compute budget, and inference requirements?
  • How can scaling laws help optimize the performance and efficiency of LLMs?

Training and Fine-Tuning LLMs

  • Main steps and challenges of training LLMs from scratch
  • Benefits and drawbacks of fine-tuning LLMs for specific tasks
  • Best practices and tools for training and fine-tuning LLMs

Deploying and Using LLMs

  • Main considerations and challenges of deploying LLMs in production
  • Common use cases and applications of LLMs in 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 of generative AI and LLMs, such as bias, misinformation, and manipulation
  • Responsible and beneficial use of generative AI and LLMs

Summary and Next Steps

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