PyTorch-Tutorial-2nd
《Pytorch实用教程》(第二版)无论是零基础入门,还是CV、NLP、LLM项目应用,或是进阶工程化部署落地,在这里都有。相信在本书的帮助下,读者将能够轻松掌握 PyTorch 的使用,成为一名优秀的深度学习工程师。
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The second edition of "PyTorch Practical Tutorial" was completed after 5 years, 4 years, and 2 years. On the basis of the essence of the first edition, rich and detailed deep learning application cases and reasoning deployment frameworks have been added, so that this book can more systematically cover the knowledge involved in deep learning engineers. As the development of artificial intelligence technology continues to emerge, the second edition of "PyTorch Practical Tutorial" is not the end, but the beginning, opening up new technologies, new fields, and new chapters. I hope to continue learning and making progress in artificial intelligence technology with you in the future.
README:
时隔5年,历时4年,耗时2年的《PyTorch实用教程》(第二版)完成了。在第一版的精华之上,增加了丰富详实的深度学习应用案例和推理部署框架,使本书更系统性的涵盖深度学习工程师所涉及的知识面。如人工智能技术发展一浪接一浪,《Pytorch实用教程》(第二版)不是结束,而是再次扬帆起航,开启新的技术、新的领域、新的篇章,希望未来能继续与大家一起在人工智能技术里学习、进步。
📚 在线阅读(开源免费):《PyTorch实用教程》(第二版)
🖥️ 配套代码(开源免费):《PyTorch实用教程》(第二版)
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本书以基础概念为基石,计算机视觉、自然语言处理和大语言模型为核心,推理部署框架为桥梁,皆在为读者提供面向项目落地的代码工程与理论讲解。本书整体分三部分,上篇:入门,中篇:应用,下篇:落地。
PyTorch基础。针对刚入门、非科班、本科生,提供PyTorch介绍,讲解开发环境的搭建,介绍PyTorch的数据、模型、优化、可视化等核心模块,最后利用所讲解的PyTorch知识点构建一套自己的代码结构,为后续的应用打下基础。
产业应用。经过上篇,磨了一把好刀,接下来就用它在各领域上大显身手。将会讲解三个主题,分别是计算机视觉(Computer Vision)、自然语言处理(Natural Language Processing)和大语言模型(Large Language Model)。
在CV章节,包括主流的任务,有图像分类、图像分割、目标检测、目标跟踪、GAN生成、Diffusion生成、图像描述和图像检索八大任务。
在NLP章节,包括RNN、LSTM、Transformer、BERT和GPT模型详解与应用,应用的任务有文本分类、机器翻译、命名体识别、QA问答和文章生成五大任务。
在LLM章节,包括4个LLM部署与代码分析和一个LLM行业应用——GPT Academic(GPT 学术优化),LLM包括国内开源的四大主流模型,Qwen、ChatGLM、Baichuan和Yi。
工业落地。有了工具,有了场景,接下来就要让它产生价值,变成可用的、好用的算法服务。因此,从pytorch这样一个训练框架、重框架中剥离出来进行部署、加速、量化是常见的方法。本章将介绍ONNX和TensorRT的原理与使用,同时借助TensorRT详细分析模型量化概念、PTQ和QAT量化实战与原理。
相信经过上、中、下篇的学习,可以帮助入门的同学少走很多弯路,快速掌握PyTorch,具备独当一面的能力,能依据实际场景选择算法模型,可以将模型部署应用,形成闭环,全流程打通。
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结构清晰:全书分为三部分:上篇(入门)、中篇(应用)、下篇(落地),逐步引导读者深入学习。
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理论与实践结合:不仅提供理论讲解,还通过丰富的项目案例,让读者能够将理论应用于实践。
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实战案例丰富:提供了计算机视觉、自然语言处理和大语言模型等多个领域的实战案例。
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系统性覆盖:涵盖PyTorch基础、计算机视觉基础任务、自然语言处理基础任务、大语言模型基础、推理部署框架。
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适用性广:适合AI自学者、AI产品经理、在校学生以及跨领域人士阅读,满足不同背景和需求的读者。
为增强读者阅读氛围,提供交流途径,特地建立了QQ交流群。
为保证群内交流质量,入群需要密码,密码获取,请查看代码
近期会在群内分享最新技术文章,包括CV项目实战,LLM推理部署,RAG系统等前沿科技,欢迎加入技术交流。
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本作品采用知识共享署名-非商业性使用 4.0 国际许可协议进行许可。
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