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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-genai-part-3-arabic-advaced-genai/
课程评论：没有评论
**课程名称：** 高级实用生成式AI (Advanced Practical GenAI) **课程概述：** 本课程是“实用生成式AI系列”的第三部分，旨在培养学员成为专业的生成式AI（GenAI）工程师/开发者。课程将从零开始，深入探讨大型语言模型（LLMs）和GenAI的基础知识，并指导学员构建可用的、生产级别的应用程序。 本系列课程强调“动手实践”，所有示例都基于代码，并通过Python、Google Colab进行分步构建，最终部署在Streamlit上。学完本系列课程后，学员将能够独立完成多种GenAI应用，包括ChatGPT克隆、Midjourney克隆、数据聊天应用、YouTube助手应用、视频问答应用、学习伴侣应用、推荐系统、GPT-V图像描述应用、DALL-E和Stable Diffusion图像生成应用、以及使用Whisper的视频评论生成应用等。 在**本部分**，学员将接触不同类型的LLMs，包括开源和闭源模型，如OpenAI的GPT系列、Meta的Llama系列、Google的Gemini和Bard、Microsoft的Orca、Mistral AI的Mixtral等。学员将学习使用预训练模型，并进行自定义数据微调。课程将深入介绍Hugging Face平台，并利用参数高效微调（PEFT）技术，特别是低秩适应（LoRA），进行高效的模型微调。此外，学员还将学习如何在云端部署模型，或为保护公司数据隐私而进行私有化托管。最后，课程还将讲解如何利用现有的预训练模型作为“教师”，通过模型蒸馏技术训练自定义模型版本。 **学习目标：** * 掌握主流LLMs的原理和应用。 * 能够使用Python和相关工具构建GenAI应用。 * 熟练运用Hugging Face进行模型微调（PEFT/LoRA）。 * 了解模型部署和私有化托管的技巧。 * 学习模型蒸馏等高级训练技术。 * 成功构建一系列实际的GenAI应用。
This is part 3 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. In this part you will work with different kinds of LLMs, being opensource or not. You will get exposed to GPT models by OpenAI, Llama models by Meta, Gemini and Bard by Google, Orca by Microsoft, Mixtral by Mistral AI and others. You will use pre-trained models, and also finetune them on your own data. We will learn about huggingface and use it for model finetuining using the Parameter Efficient Training or PEFT models. We will use Low-Rank Adaptation or LoRA for efficient training. You will learn how to deploy a model in the cloud, or privately host it for privacy concerns of your company data. You will learn how to use existing pre-trained models as teachers and use model distillation to train your custom version of the model.
