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Generative AI with Large Language Models

(CTU-AI325.AJ1)
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Skills You’ll Get

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1

Fundamentals of Generative AI and LLMs

  • Foundation Models
  • A brief history of how transformers were born
  • The new role of AI professionals
  • The rise of seamless transformer APIs
  • The rise of the Transformer: Attention Is All You Need
  • Training and performance
  • Hugging Face transformer models
2

Optimization Techniques for Scalable LLMs

  • Why Mixture of Experts?
  • Architecture & Algorithmic Design
  • System Engineering Challenges
  • Why Knowledge Distillation?
  • Comparison between Knowledge Distillation and Traditional Approaches
  • Distillation Strategies: Architectures of Transfer
3

Fine-Tuning LLMs for Domain-Specific Tasks

  • GPTs as GPTs
  • The architecture of OpenAI GPT transformer models
  • OpenAI models as assistants
  • Getting started with the GPT-4 API
  • Retrieval Augmented Generation (RAG) with GPT-4
4

Retrieval-Augmented Generation (RAG) Systems

  • The architecture of BERT
  • Fine-tuning BERT
  • Building a Python interface to interact with the model
  • Risk management
  • Fine-tuning a GPT model for completion (generative)
  • Preparing the dataset
  • Fine-tuning an original model
  • Running the fine-tuned GPT model
  • Managing fine-tuned jobs and models
  • Before leaving
5

Controlling Hallucination and Ensuring Factuality in LLMs

  • The emergence of functional AGI
  • Cutting-edge platform installation limitations
  • Auto-BIG-bench
  • WandB
  • When will AI agents replicate?
  • Risk management
  • Risk mitigation tools with RLHF and RAG

1

Fundamentals of Generative AI and LLMs

  • Training, Evaluating, and Visualizing a Machine Learning Classifier
  • Implementing Multi-Head Attention and Post-Layer Normalization
  • Exploring Positional Encoding in Transformer Models
2

Optimization Techniques for Scalable LLMs

  • Simulating Efficiency in Dense vs Sparse MoE Models
  • Implementing a Top-k Gating Router for MoE
  • Debugging an Expert Collapse in MoE
  • Implementing Real-Time NLP Sentiment Analysis Using Attention-Based Distillation
  • Visualizing the Transfer of Knowledge Between Teacher and Student Models
3

Fine-Tuning LLMs for Domain-Specific Tasks

  • Analyzing GPT Transformer Architecture and OpenAI Model APIs
  • Getting Started with OpenAI GPT-4 for NLP Tasks
  • Implementing RAG Using GPT-4
4

Retrieval-Augmented Generation (RAG) Systems

  • Fine-Tuning BERT for Sentence Classification Using the CoLA Dataset
5

Controlling Hallucination and Ensuring Factuality in LLMs

  • Evaluating Auto-BIG-bench Tasks
  • Evaluating and Mitigating Hallucination in RAG Systems
  • Mitigating Risks in Generative AI Systems

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