ATC-GENAILLM - Generative AI with Models and LLM - Intermediate

Generative AI involves creating models that can generate new, realistic data, such as images, text, or audio, rather than simply recognizing patterns.

To optimize this we need to understand how Generative AI work and how to improve its accuracy which can be tuned with various prompts and choosing right Model

Generative AI Intermediate course encompass understanding from the engineering and developer point of view to integrate Generative AI models in Application.

Duration: 3.0 days

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Objectives

The idea of this course is to understand fundamentals of Generative AI and it's underline functionality of improving prompt engineering and working with different LLMs and Generative AI models.

Content

Module 1: Advanced Prompt Engineering Techniques

  • What is a Prompt?
  • Intuition Behind Prompts
  • Everyone Can Program with Prompts
  • Prompt Patterns
  • The Persona Pattern
  • Introducing New Information to the Large Language Model
  • Prompt Size Limitations
  • Prompts are a Tool for Repeated Use
  • Root Prompts

Module 2: Prompt Flow Design Learning Studio

  • Question Refinement Pattern
  • Cognitive Verifier Pattern
  • Audience Persona Pattern
  • Flipped Interaction Pattern

Module 3: Introduction to Deep Learning

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Deep Learning Models
  • Applications of Generative Deep Learning

Module 4: Prompt Engineering with GPT4

  • Fine-Tuning GPT4 with Custom Examples
  • Using ChatGPT
  • Using Google Bard
  • Others- GitHub Copilot, OpenAI Codex

Module 5: Variational Autoencoders (VAEs)

  • What are Variational Autoencoders (VAEs)
  • Architecture Variational Autoencoders
  • Advantages of VAEs
  • Challenges of VAEs
  • Applications of VAEs

Module 6: Fine-Tuning Open-Source LLMs

  • LLM Cost optimization & performance.
  • Deploying Custom LLMs to the Cloud

Module 7: Azure OpenAI Service and form recognizer

Module 8: ChatGPT Copilot using Azure OpenAI Studio

Module 9: Semantic Kernel with Azure OpenAI

Module 10: Amazon Bedrock

  • What is Amazon Bedrock?
  • Benefits of using Amazon Bedrock
  • Interfaces & Prompts
  • Amazon Bedrock Agent
  • Amazon Titan
  • Use cases : Amazon Titan
  • Titan Model Versions

Module 11: Combining Transformers

  • What are Transformers
  • Attention is all you need – Architecture
  • Applications of Transformers
  • RNNs vs Transformer

Audience

This program is beneficial to professionals from a variety of industries and backgrounds. Those who are looking for a career into the field of GenAI, ML/DL, who have knowledge or prior experience in programming and mathematics, and an analytical frame of mind will be added advantage.

Prerequisites

Knowledge on GenAI, ML/DL.

Certification

product-certification

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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