用OpenAI、ChatGPT与Python掌握AI应用开发

本课程教你使用Python集成OpenAI API、提示工程、函数调用与结构化输出，快速掌握AI应用开发，适合有Python基础的开发者。

原始标题：Ai Application Development With Openai, Chatgpt, And Python

Published 10/2024

MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Language: English | Size: 3.59 GB | Duration: 6h 3m

Master AI App Development using OpenAI API integration, Prompt Engineering, Function Calling and Structured Outputs.

What you’ll learn
Learn to interact with OpenAI Platform (Generative AI) using Python Code
Learn the LLM basics, ChatGPT evolution, training, and practical usage.
Learn to work and explore the multimodal capabilities such as images, files, audio using OpenAI and Python code.
Learn to use Prompt Engineering to guide AI models in generating accurate outputs.
Learn to use latest techniques to generate the Structured Outputs from LLM
Learn to use the power of function calling with OpenAI to interact with external systems

Requirements
Experience with Python
Experience working with IDE such as Visual Studio Code.

Overview
Section 1: Getting Started With the Course

Lecture 1 Course Introduction

Lecture 2 Pre-requisites

Section 2: Course Slides and Source Code

Lecture 3 Course slides

Lecture 4 Source Code

Lecture 5 The World Before LLMs: A Glimpse into the Past?

Lecture 6 Large Language Models and its Evolution

Lecture 7 How are LLMs Models Trained ?

Lecture 8 GPT Models and its Evolution

Lecture 9 Advantages, Challenges and Applications using LLMs

Lecture 10 Sign up for a ChatGPT Account and Start Exploring

Section 4: OpenAI APIs: Your First Steps to Mastery

Lecture 11 Introduction to OpenAI API

Lecture 12 Setup OpenAI Account & Open AI Playground

Lecture 13 Setup python in Mac

Lecture 14 Setup python in Windows

Lecture 15 Set up the Base Project using Poetry

Lecture 16 Set up OpenAI APIKey

Lecture 17 Interact with GPT using OpenAI Client

Lecture 18 Structuring API Calls with Functions

Lecture 19 Prompt, Tokens and Tokenization – What are they ?

Lecture 20 OpenAI Request Parameters – temperature

Lecture 21 OpenAI Request Parameters – max_tokens

Lecture 22 OpenAI Request Parameters – top_p

Lecture 23 Streaming OpenAI Responses

Lecture 24 Understanding System, Assistant, and User Messages in OpenAI

Lecture 25 System, Assistant, and User Messages in Action

Section 5: Mastering Multimodality: Creating and Editing Images with OpenAI

Lecture 26 Introduction to MultiModality in AI

Lecture 27 Creating an Image using OpenAI

Lecture 28 Refactor Code to Write the Image in the file system

Lecture 29 Create an variation using the “create_variation” function

Section 6: Mastering Multimodality: Exploring Vision Capabilities with OpenAI

Lecture 30 Unlocking Vision: Image Understanding Capabilities using Image URL

Lecture 31 Unlocking Vision: Understanding Capabilities – using Encoded Image

Lecture 32 Vision API Limitations

Section 7: Mastering Multimodality: Creating and Processing Audio with OpenAI

Lecture 33 From Speech to Sound: Converting Text to Voice with OpenAI’s TTS Model

Lecture 34 Introduction to the Whisper API in OpenAI

Lecture 35 Transcribing Speech using Whisper API in OpenAI

Lecture 36 Translation using Whisper API in OpenAI – Translate from French to English

Section 8: Prompt Engineering

Lecture 37 Prompt Engineering & PromptTemplate

Lecture 38 Set up Project for Prompt Engineering

Lecture 39 Prompt Engineering in Action – Lets explore the Travel Plan Prompt

Lecture 40 Understanding Prompt Injection and How to Mitigate It

Lecture 41 Zero Shot Prompting

Lecture 42 Few Shot Prompting

Lecture 43 Chain of Thought Prompting

Lecture 44 Mastering Multi-Step Prompts

Section 9: Generating Structured Data with OpenAI

Lecture 45 Introduction to Structured Outputs in LLM

Lecture 46 Structured outputs using Prompt Engineering

Lecture 47 Structured outputs with Few Shot Examples using Prompt Engineering

Lecture 48 Pydantic in Action

Lecture 49 Structured outputs with Pydantic Model Validations using Prompt Engineering

Lecture 50 Structured outputs using response_format and pydantic

Section 10: Function Calling using tools with OpenAI

Lecture 51 Function Calling – What & Why ?

Lecture 52 Function Calling with OpenAI: Accessing System Name and Time

Lecture 53 Building an Interactive Command-Line App

Lecture 54 Connecting OpenAI Function Calling to Open Meteo API for Realtime Weather Data

Lecture 55 Real-time Stock Price Retrieval using OpenAI Function Calling

Software Developers looking to integrate AI capabilities into their applications using OpenAI and Python.,Data Scientists interested in enhancing their skill set with AI application development.,AI Enthusiasts who want to explore practical implementations of OpenAI’s APIs and ChatGPT.,Machine Learning Engineers aiming to expand their knowledge by incorporating language models into projects.,Entrepreneurs and Startups aiming to build AI-based products or services quickly and efficiently.,Students and Graduates in computer science or related fields who want hands-on experience with AI applications.,Anyone Curious About AI with a foundational understanding of Python and a desire to learn about AI application development.