Generative AI: OpenAI API, DeepSeek, and ChatGPT in Python

Overview

Empower Your Business With GenAI, Artificial Intelligence, GPT-4o, o1, o3, DeepSeek, and More! What you'll learn:

How to setup and use the OpenAI API with ChatGPTHow to effectively use prompt engineeringRAG (retrieval-augmented generation) with the OpenAI Embeddings APIFAISS (Facebook AI Similarity Search)How to Fine-Tune ChatGPT Welcome to the forefront of artificial intelligence with our groundbreaking course on Generative AI (GenAI), the OpenAI API, DeepSeek, and ChatGPT.

With ChatGPT and DeepSeek, you'll learn how to build with the world's most advanced Large Language Models (LLMs). This course is a must-have if you want to know how to use this cutting-edge technology for your business and work projects.This course contains 5 main sections:

Basic API Usage:

All the fundamentals:

signup for an account, get your API key, set environment variables on Windows / Linux / Mac, using the API in Python, setup billing, understand the pricing model, and OpenAI's usage policies.

Of note is the chatbot tutorial, which goes over how to incorporate chat history into the model so that ChatGPT "remembers" what it said to you previously. A customer service chatbot will serve as a running example throughout this course.Prompt Engineering:

ChatGPT Prompt Engineering for Developers - All about how to make ChatGPT do what you want it to do.

We'll explore various example use-cases, such as getting ChatGPT to output structured data (JSON, tables), sentiment analysis, language translation, creative writing, text summarization, and question-answering. We'll explore techniques like chain-of-thought (CoT) prompting, and we'll even look at how to use ChatGPT to build a stock trading system!Retrieval Augmented Generation (RAG):

Learn how to incorporate external data into LLMs.

This powerful technique helps mitigate a common problem called "hallucination". It's critical if you have proprietary data (like product info for your company) that your LLM doesn't know about.

You'll learn how semantic search / similarity search works, and how to implement it using FAISS (Facebook AI Similarity Search library). Learn how this will allow you to "chat with your data".Fine-Tuning:

Learn how to "train" an LLM on your own dataset so that it behaves the way you want it to.

Sometimes prompt engineering and RAG won't cut it.GPT-4 with Vision:

Everything in this course can be done with GPT-4, but what makes GPT-4 (and GPT-4 Turbo) special is its vision capabilities. That is, it can understand images.

In this section, we'll explore many of the amazing applications of combined text-image understanding, some of which include automated homework grading, explaining memes and humor, handwriting transcription, web development, game development, and writing product descriptions based on images (business owners - you already know how this will skyrocket your productivity).Throughout this course, you'll engage in hands-on exercises, real-world applications, and expert guidance to solidify your understanding and mastery of generative AI concepts. Whether you're a seasoned developer, aspiring AI enthusiast, or industry professional, this course offers a transformative experience that will empower you to harness the true potential of AI.Are you ready to embark on this exhilarating journey into the future of AI?

Join us and unlock the endless possibilities of Generative AI today!Suggested Prerequisites:

Python coding

Syllabus

• Introduction to the Course
Course overview and objectives
Prerequisites and requirements
• Basic API Usage
Setting up an OpenAI account and obtaining an API key
Configuring environment variables (Windows/Linux/Mac)
Using the OpenAI API in Python
Setting up billing and understanding the pricing model
Overview of OpenAI's usage policies
Building a customer service chatbot with chat history
• Prompt Engineering
Basics of prompting and best practices
Use-cases: JSON output, tables, sentiment analysis
Language translation and creative writing
Text summarization and question-answering
Chain-of-thought (CoT) prompting
Developing a stock trading system with ChatGPT
• Retrieval Augmented Generation (RAG)
Introduction to RAG and data incorporation
Mitigating hallucination in AI models
Semantic search and similarity search concepts
Implementing RAG using FAISS (Facebook AI Similarity Search)
Applications: "Chat with your data"
• Fine-Tuning
Overview and benefits of fine-tuning
Training an LLM on custom datasets
Limitations and when to use fine-tuning over prompt engineering
• GPT-4 with Vision
Introduction to GPT-4's vision capabilities
Text-image understanding applications
Automated homework grading and meme explanation
Handwriting transcription
Web and game development
Creating product descriptions from images
• Hands-On Exercises and Real-World Applications
Practical projects and exercises
Real-world examples and case studies
• Conclusion and Next Steps
Recap of key learnings
Resources for further learning and exploration
Feedback and course wrap-up

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Taught by

Lazy Programmer Inc. and Lazy Programmer Team

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Subjects

Computer Science