Developing Generative AI Applications with Python and Open AI
Generative AI is a turning point in human history. Those who leverage LLMs will be more productive, creative, efficient, and will be able to achieve more with less. In this course you will learn how to create generative AI applications with the OpenAI API and Python.
What you'll learn
Generative AI is a turning point in human history. Those who leverage LLMs will be more productive, creative, efficient, and will be able to achieve more with less. In this course, Developing Generative AI Applications with Python and OpenAI (ChatGPT), you’ll gain the ability to create generative AI applications. First, you’ll learn about the fundamentals of generative AI models, including their architecture, training processes, and applications. At this point you’ll learn how to write good prompts, which is an extremely valuable skill. Next, you’ll familiarize yourself with the OpenAI API and the available models, third you’ll use the API to generate human-like responses to questions or generate content based on your prompts. Moving forward, you will learn how to create a basic chatbot. Finally, you’ll learn how to train a model using your own data. When you’re finished with this course, you’ll have the skills and knowledge of how to create a generative AI application using the OpenAI API and Python.
Table of contents
- Understanding Generative Pre-Trained Transformer (GPT) Models 3m
- Architecture of Large Language Models 4m
- Transformer Architecture in Simpler Words 2m
- Evolution Of GPT Model Family 4m
- Impact of Model Size on Performance 2m
- Basics Of Generative Pre-Trained Transformer (GPT) Models 3m
- Overview of Rival Language Models (LLMs) 3m
- Demo From ChatGPT to OpenAI API 3m
- Let's Talk APIs 1m
- The OpenAI API at a High Level 3m
- Getting Started: The OpenAI Platform Starter Pack 2m
- Using the OpenAI API with Python 4m
- Using the OpenAI Python Library 4m
- Anatomy of an API Call 5m
- Listing and Retrieving Models 3m
- Model Pricing 2m
- Using the Chat Completions API, Few Shot Learning, Zero Shot Learning, Objects, Streaming, and More 5m
- Creating Images 2m
- Editing Images 2m
- Image Variations 1m
- Creating Embeddings 1m
- Transcribing Text 2m
- Translating Text 2m
- Moderating Text 1m
- Working With Files 2m
- Prompt This, Prompt That 1m
- Understanding Prompting 2m
- Anatomy of an Effective Prompt 4m
- Tokenization 4m
- The OpenAI Playground 4m
- Prompting Best Practices a.k.a. Crafting Effective Prompts 4m
- Six Best Practices, Starting with Write Clear Instructions 5m
- Provide Reference Text 1m
- Split Complex Tasks into Simpler Subtasks 2m
- Give GPTs Time to Think 3m
- Use External Tools and Test Changes Systematically 2m
- A Few Final Words On Prompt Engineering 2m
- Leveraging The API's Capabilities: Practical Applications 1m
- Practical Applications of the OpenAI API 2m
- Summarization 4m
- Classification 3m
- Sentiment Analysis 3m
- Named Entity Recognition (NER) 2m
- Keyword Extraction 2m
- Language Translation 3m
- Text Parsing 1m
- Paraphrasing 2m
- Real World Applications 1m
About the author
Xavier is very passionate about teaching, helping others understand search and Big Data. He is also an entrepreneur, project manager, technical author, trainer, and holds a few certifications with Cloudera, Microsoft, and the Scrum Alliance, along with being a Microsoft MVP. He has spent a great deal of his career working on cutting-edge projects with a primary focus on .NET, Solr, and Hadoop among a few other interesting technologies. Throughout multiple projects, he has acquired skills to deal... more