|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/web-applications-with-large-language-model-fast-inference/
课程评论：没有评论
课程名称：掌握生成性人工智能与大语言模型部署 概述：本课程深入探讨生成性人工智能的前沿科学挑战，旨在帮助学员发现当前存在的问题，并开发或定制自己的大型模型应用程序。课程适合任何对大语言模型及其部署充满热情的候选人，包括学生、工程师和专家。学员将深入了解 TensorFlow、PyTorch、Keras 模型、Hugging Face 以及 Docker 服务。此外，学员将学会如何优化和量化 TensorRT 框架，以便在各种领域进行部署。同时，他们还将学习如何将量化的 LLM 模型部署到使用 React、JavaScript 和 FLASK 开发的网页上。课程将教授如何将强化学习（PPO）集成到大型语言模型中，以基于人类反馈进行微调。候选人将能够熟练使用 C/C++ 编程语言进行编码和调试，至少达到中级水平。 使用的 LLM 模型包括：Falcon、LLAMA2、BLOOM、MPT、Vicuna、FLAN-T5、GPT2/GPT3、GPT NEO、X BERT 101、Distil BERT。课程还将涵盖对小型模型的微调，并在大型模型的监督下进行。 图像生成方面，学员将学习使用 LLAMA 模型、Gemini、Dall-E、OpenAI 及 Hugging Face 模型。课程内容包括从零开始学习和安装 Docker、JavaScript、HTML、CSS、Bootstrap、React Hook、DOM 和 JavaScript 网页开发。深入研究基于深度学习的变换器自然语言处理，Python FLASK REST API 及 MySQL。课程还将讲解 DockerFiles 和 Docker Compose 的准备及调试文件，Visual Studio Code 插件包的配置和安装，以及 TensorFlow、PyTorch、Keras 等框架的学习、安装和配置。 课程将涵盖深度学习数据集的预处理与准备、使用 C++ 进行 OpenCV DNN 推理、深度学习框架的训练、测试和验证、将预构建模型转换为 ONNX 及其在 C++ 编程中的推理。学员将学习将 ONNX 模型转换为 TensorRT 引擎，以及使用 C++ 运行时和编译时 API 进行推理。此外，学员将比较 TensorRT 和 ONNX 推理之间的测量指标与结果，做好 C++ 面向对象编程推理的准备。 本课程强调解决边缘设备和云端领域的部署问题，涵盖大语言模型的微调及大量实操练习。学员将学习 BLOOM、GPT3-GPT3.5、FLAN-T5 系列模型的训练、评估及自定义提示的上下文学习/在线学习，以及如何在大型多任务 LLM 模型中避免灾难性遗忘，并准备 LLM 应对代码生成、摘要、内容分析及图像生成等多任务问题。 重要提示：本课程不提供复制和粘贴的内容，学员需要亲自动手完成每一行项目，以成功成为 LLM 和网页应用开发者！学员无需任何特殊硬件组件，项目可以在云端或本地计算机上交付。
This course is diving into Generative AI State-Of-Art Scientific Challenges. It helps to uncover ongoing problems and develop or customize your Own Large Models Applications. Course mainly is suitable for any candidates(students, engineers,experts) that have great motivation to Large Language Models with Todays-Ongoing Challenges as well as their deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge on TensorFlow , Pytorch, Keras models, HuggingFace with Docker Service. In addition, one will be able to optimize and quantize TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of LLM quantized model to Web Pages developed with React, Javascript and FLASKHere you will also learn how to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based. Candidates will learn how to code and debug in C/C++ Programming languages at least in intermediate level.LLM Models used: The Falcon, LLAMA2, BLOOM, MPT, Vicuna,FLAN-T5, GPT2/GPT3, GPT NEOXBERT 101, Distil BERTFINE-Tuning Small Models under supervision of BIG ModelsImage Generation:LLAMA modelsGemini Dall-E OpenAIHugging face ModelsLearning and Installation of Docker from scratchKnowledge of Javscript, HTML ,CSS, BootstrapReact Hook, DOM and Javacscript Web DevelopmentDeep Dive on Deep Learning Transformer based Natural Language ProcessingPython FLASK Rest API along with MySqlPreparation of DockerFiles, Docker Compose as well as Docker Compose Debug fileConfiguration and Installation of Plugin packages in Visual Studio CodeLearning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratchPreprocessing and Preparation of Deep learning datasets for training and testingOpenCV DNN with C++ InferenceTraining, Testing and Validation of Deep Learning frameworksConversion of prebuilt models to Onnx and Onnx Inference on images with C++ ProgrammingConversion of onnx model to TensorRT engine with C++ RunTime and Compile Time APITensorRT engine Inference on images and videosComparison of achieved metrices and result between TensorRT and Onnx InferencePrepare Yourself for C++ Object Oriented Programming Inference!Ready to solve any programming challenge with C/C++ Read to tackle Deployment issues on Edge Devices as well as Cloud AreasLarge Language Models Fine TunningLarge Language Models Hands-On-Practice: BLOOM, GPT3-GPT3.5, FLAN-T5 familyLarge Language Models Training, Evaluation and User-Defined Prompt IN-Context Learning/On-Line LearningHuman FeedBack Alignment on LLM with Reinforcement Learning (PPO) with Large Language Model: BERT and FLAN-T5How to Avoid Catastropich Forgetting Program on Large Multi-Task LLM Models.How to prepare LLM for Multi-Task Problems such as Code Generation, Summarization, Content Analizer, Image Generation.Quantization of Large Language Models with various existing state-of-art techniquesImportante Note: In this course, there is not nothing to copy & paste, you will put your hands in every line of project to be successfully LLM and Web Application Developer!You DO NOT need any Special Hardware component. You will be delivering project either on CLOUD or on Your Local Computer.
