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Modern Generative AI with ChatGPT and OpenAI Models
Duration
3 Days
Course
PP-DSGE-100
Available Formats
Classroom Training, Online Training
Course Description
Overview
Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This course will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You’ll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you’ll explore use cases where ChatGPT can boost productivity and enhance creativity. You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this course to your own challenges faster. You’ll also discover enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this course, you’ll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models’ APIs in your own projects.Objectives
Topics
- Introduction to Generative AI
- Introducing generative AI
- The history and current status of research
- Technical requirements
- What is OpenAI?
- An overview of OpenAI model families
- Road to ChatGPT: the math of the model behind it
- ChatGPT: the state of the art
- Setting up a ChatGPT account
- Familiarizing yourself with the UI
- Organizing chats
- What is a prompt and why is it important?
- Zero-, one-, and few-shot learning – typical of transformers models
- Principles of well-defined prompts to obtain relevant and consistent results
- Avoiding the risk of hidden bias and taking into account ethical considerations in ChatGPT
- Technical requirements
- ChatGPT as a daily assistant
- Generating text
- Improving writing skills and translation
- Quick information retrieval and competitive intelligence
- Why ChatGPT for developers?
- Generating, optimizing, and debugging code
- Generating documentation and code explainability
- Understanding ML model interpretability
- Translation among different programming languages
- Technical requirements
- Marketers’ need for ChatGPT
- New product development and the go-to-market strategy
- A/B testing for marketing comparison
- Boosting Search Engine Optimization (SEO)
- Sentiment analysis to improve quality and increase customer satisfaction
- Researchers’ need for ChatGPT
- Brainstorming literature for your study
- Providing support for the design and framework of your experiment
- Generating and formatting a bibliography
- Generating a presentation of the study
- Technical requirements
- OpenAI and Microsoft for enterprise-level AI – introducing Azure OpenAI
- Why introduce a public cloud?
- Understanding responsible AI
- Technical requirements
- How Azure OpenAI is being used in enterprises
- Contract analyzer and generator
- Understanding call center analytics
- Exploring semantic search
- Recap of what we have learned so far
- This is just the beginning
- The impact of generative technologies on industries – a disruptive force
- Unveiling concerns about Generative AI
- Ethical implications of Generative AI and why we need Responsible AI
- What to expect in the near future
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
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title
Please take a moment to fill out this form. We will get back to you as soon as possible.
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