|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-genai-part-2-arabic-rag/
课程评论：没有评论
课程名称：多模态生成AI RAG应用 课程概述：这是实践生成AI续集的第二部分，该课程旨在培养专业的生成AI工程师/开发者。课程从基础开始，深入LLM和生成AI领域，采取“动手实践”的方式进行教学。所有示例均为基于代码，学员将逐步构建可用的生产级应用程序，使用Python、Google Colab和Streamlit进行部署。通过本课程，学员将构建多个项目，包括ChatGPT克隆、Midjourney克隆、数据聊天应用、YouTube助手应用、视频问答、学习伙伴应用、推荐系统、使用GPT-V的图像描述应用，以及使用DALL-E和StableDiffusion的图像生成应用，还将构建利用Whisper的自动评论视频应用等。 课程将涵盖提示工程的方法，并运用这些方法构建定制应用，超越ChatGPT的能力。学员将使用OpenAI API、LangChain等多种工具，利用Streamlit作为用户界面和云部署框架进行应用开发。Streamlit以其易用性和简单的Python编写方式而闻名。借助GPT模型（无论是OpenAI的还是开源的，如Huggingface上的Llama或Mixtral），学员将能够构建有趣的应用，例如与文档对话、与YouTube视频对话、构建最先进的推荐系统、视频自动评论以及语音翻译等。 此外，课程还将结合多种模态，例如使用GPT-V和DALL-E的图像与生成文本的ChatGPT（GPT-3.5和GPT-4），以及利用Whisper的语音。学员将能够为AI模型提供自定义数据，这些数据在互联网上不可获得，而开源模型如ChatGPT并不知晓。课程将深入探讨先进和前沿的主题，如RAG模型和LLM代理。 总之，本课程将从基础入手，逐步引导学员实践和构建多种生成AI应用，助力成为该领域的专业人才。
This is Part 2 of the Practical GenAI Sequel.The objective of the sequel is to prepare you to be a professional GenAI engineer/developer. I will take you from the ground-up in the realm of LLMs and GenAI, starting from the very basics to building working and production level apps. The spirit of the sequel is to be "hands-on". All examples are code-based, with final projects, built step-by-step either in python, Google Colab, and deployed in streamlit. By the end of the courses sequel, you will have built chatgpt clone, Midjourney clone, Chat with your data app, Youtube assistant app, Ask YouTube Video, Study Mate App, Recommender system, Image Description App with GPT-V, Image Generation app with DALL-E and StableDiffusion, Video commentator app using Whisper and others. We will cover prompt engineering approaches, and use them to build custom apps, that go beyond what ChatGPT knows. We will use OpenAI APIs, LangChain and many other tools. We will build together different applications using streamlit as our UI and cloud deployment framework. Streamlit is known of its ease of use, and easy python coding.With the power of GPT models, either by OpenAI, or opensource (like Llama or Mixtral on Huggingface), we will be able to build interesting applications, like chatting with your documents, chatting with youtube videos, building a state-of-the art recommender systems, video auto commentator and translator from one voice to another. We will mix modalities, like Image with GPT-V and DALL-E, Text with ChatGPT: GPT3.5 and GPT4, and voice with Whisper. We will be able to feed the AI models with our custom data, that is not available on the internet, and open models like ChatGPT do not know about. We will cover advanced and state-of-the art topics like RAG models, and LLM Agents.
