用AWS与Python实现生成式AI研发：从理论到实战 – CG群

学习使用Bedrock、LLM、LangChain、RAG和Streamlit构建AI应用与聊天机器人，涵盖深度学习基础、多模态AI及文本生成图像等实战案例，适合所有水平开发者。

原始标题：Generative AI for Research & Development with AWS, Python

Generative AI for Research & Development with AWS, Python

Learn to build AI apps and chatbots using Bedrock, LLMs, LangChain, RAG, Python, Streamlit, and Generative AI for RD.

What you’ll learn
Introduction to AI, ML, and Neural Networks
Students will gain insight into real-world applications of AI.
Students will gain an understanding of the foundations of Deep Learning.
Learn how Generative AI works and deep dive into Foundation Models.
Students will learn about Foundation Models, LLMs, Text-to-Image generation, and Multimodal AI, and their real-world applications.
Students will learn to use Amazon Bedrock Console, Playgrounds, Builder Tools, Safeguard, and models.
Use Case 1: Text-to-image generation with AWS Lambda and Amazon AI models, including setup.
Use Case 2: Text-to-image generation with AWS Lambda and Stable Diffusion AI models.
Use Case 3: Text summarization using Cohere Command and Text Foundation Models.
Use Case 4: Python-Based Chatbot with AWS Bedrock and Anthropic Claude FM
Use Case 5: Streamlit-Based Python Chatbot with AWS Bedrock and Anthropic Claude
Use Case 6: LangChain-Driven Streamlit Chatbot Using Python, AWS Bedrock, Anthropic Claude
Use Case 7: Retrieval Augmented Generation (RAG) – Build a Health Chatbot
Project – Text2Speech Player, students will develop a Text-to-Speech (TTS) player using Python libraries such as gTTS, os, and pygame.
Python coding practice
Regular Expression (regex) in Python
Mastering Keywords in Python
How to declare and assign values to variables.
Python Functions: Definition and Usage
How to Begin Practicing Python Coding
Return Statement in Python

Requirements
Basic Computer Skills: Familiarity with using a computer and navigating the internet.
You need to have an AWS account.
A basic understanding of Python is required.

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