ChatGPT exploded, why is it so amazing? How should I learn? How many steps does it take to learn? Under the upsurge of ChatGPT, how to make course teaching catch the ChatGPT express train, and easily build AI courses and practical teaching content that conform to cutting-edge technology trends?
come here! Qingruan's "U+Artificial Intelligence" training platform includes course materials that deeply deconstruct the underlying technical principles of ChatGPT. From courses to experiments to project practice, the one-stop teaching system is full of sincerity and makes the principles of ChatGPT easy to teach and learn. Hurry up Come and experience it!
Writing stories, writing codes, writing papers, writing scripts, writing copywriting, writing PPT...ChatGPT is constantly opening up people's new brain holes on the way to becoming popular. If the traditional NLP model is a "domain expert" who only makes achievements in a specific field, then ChatGPT is like a professional consultant who knows astronomy and geography, and a close old friend who can accompany you at any time. He can also be an artist with a burst of imagination, and he is like an omnipotent existence like a great devil.
So, how did ChatGPT develop into its current "ultimate form"?
The birth of ChatGPT is inseparable from the support of large models. It is based on the GPT-3.5 large-scale language model and fine-tunes the model through artificial feedback reinforcement learning, so that it can accurately understand human intentions and express in the most suitable way. Generate answers in a customary way. Its evolution process covers various progressive technical stages such as Transformer, BERT, and GPT. If you want to systematically learn the underlying principles and application skills of ChatGPT, the best way is to follow the technological evolution route to develop advanced study.
ChatGPT technology evolution route
01 Advanced course system, easy to teach the technical principles of large models
The U+ artificial intelligence training platform provides colleges and universities with a one-stop artificial intelligence talent training solution covering curriculum design, experimental training, and project combat. It has been developed and designed into the course "Natural Language Processing Based on Deep Learning" to help teachers build a clear and complete The teaching system can easily lead students to understand the underlying knowledge of natural language processing, learn systematically from shallow to deep, and master related technical points of large models. This course not only provides references for detailed theoretical knowledge about ChatGPT, but also systematically sorts out the basic tasks and main algorithms of natural language processing, covering the whole process from theoretical courses to application experiments, to actual combat of enterprise projects, linking learning and practice The complete teaching process makes the teacher's teaching more interesting and convenient, and helps students better learn the knowledge related to the large model and apply it on the ground.
The course sets up teaching contents such as "Seq2seq2 structure based on Transformer", "language model based on two-way LSTM - ELMo", "language model based on two-way Transformer - BERT", "language model based on one-way Transformer - GPT", and ChatGPT The technological evolution route fits closely, and the advanced learning path including Transformer, ELMo, BERT, GPT, ERNIE, XLNet and other model knowledge is carefully designed, and the technical points involved in each model are explained from the shallower to the deeper, such as From Transformer's workflow, Self-Attention, Multi-Head Attention, Encoder&Decoder structure, to GPT's model architecture, model pre-training, model fine-tuning, etc., the technical principles of large models are easier to teach and learn through the explanation of closely connected knowledge points.
curriculum structure
In terms of course content design, starting from the real application process in the industry, it builds in detail from the principle of the large model framework, to pre-training, model fine-tuning, to the relationship between each large model and the bias of the application scenario, as well as the text classification combined with the large model, Sentiment analysis, text similarity, machine translation, text generation, intelligent question answering and other downstream application scenarios help teachers build a knowledge chain related to large model applications.
Course content design
课程将技术点细致化拆解为多个环环相扣的知识单元，并用图形化方式展现出来，减少教学过程中的理解断层。如将Transformer结构的讲解拆分成了Encoder、Decoder、Embedding、Self-Attention等多个知识单元，并以图形化方式形象展现模型原理。通过将模型结构拆解成知识单元的方式去组织相关教学内容，帮助教师轻松教授结构复杂的模型原理，让学生更深入地理解学习大模型所涉及的技术要点。
图形化知识单元
02 大模型落地应用实验，使教学内容更丰富
《基于深度学习的自然语言处理》课程还配备了《基于迁移学习的新闻分类》实验内容，通过新闻分类的行业热点应用案例，丰富教师的教学内容，帮助教师引导学生学习、了解大模型在特定领域的落地应用方式，使学生逐步掌握使用深度学习框架搭建大模型，以及基于大型预训练模型进行迁移学习的方法。实验从大模型所解决的实际应用问题出发，即某些特定领域不具备足够的数据，利用深度模型不能很好地学习，从而明确实验目标：通过将其它领域训练好的大模型迁移过来，再使用该模型进行微调的方式，使模型能在很好地拟合少量数据的同时又具备较好的泛化能力。
基于该实验目标，详细讲解如何在预训练的BERT模型和ERNIE模型上进行迁移学习，通过微调大规模预训练语言模型实现新闻文本分类。实验包含从数据准备、模型下载、数据预处理，到模型搭建、模型训练、模型验证、模型预测等完整流程，并对每个实验环节进行细粒度的拆解，细致梳理实际编程中的代码作用和注意要点，引导学生参照手册自主完成实验，使学生通过应用实操，熟练掌握大模型的预训练+微调的实际应用方式。
实验手册
同时平台具备全流程实训管理工具，方便教师及时了解每个人的学习情况。学生可一键进入实验环境，操作过程中遇到问题可随时申请远程协助，并可通过自动评测功能，对模型效果进行自测检验，还可以根据模型得分调整优化模型，大大减轻了教师的工作量。
实验环境
03 大模型企业实战项目，育人就业无缝衔接
课程引入真实的企业实战案例，有助于教师强化「双师」素质，进一步提高教学能力和指导学生实训、实践的能力，帮助教师搭建高质量的实践教学体系，培养出复合型、应用型人才。
该课程配备了《基于知识图谱的农医对话系统》的真实企业项目，利用水稻疾病知识语料，基于Neo4j图数据库搭建了一个小型的水稻疾病知识图谱，并在知识图谱的基础上搭建了一个对话系统，输入水稻疾病感染时的症状，系统便会从知识图谱中查询水稻疾病的类型，并组织成自然语言返回。通过搭建Web网站——水稻疾病智能问诊平台，用户发送描述水稻症状的消息后，系统会请求后端的对话生成服务，并将查询结果返回给用户。此外还搭建了一个微信公众号，可通过公众号实现水稻疾病的自动问诊，以进行对症下药，有效地保护水稻的生长。
基于知识图谱的农医对话系统
项目完整还原了企业端如何利用大模型进行微调以满足企业需求，让学生置身真实的企业工作场景，培养学生将实战训练与岗位能力相结合的能力。从项目初期设计，数据收集、命名实体识别数据标注、微调中文通用信息抽取模型UIE进行命名实体识别、搭建水稻疾病知识图谱、基于BERT训练句子相关性判断模型、模型部署、使用对话平台UNIT创建聊天机器人、搭建水稻疾病在线问诊平台的完整流程，可以引导学生体验大模型在企业项目中的真实运用流程，提升大模型的项目实战能力和工程思维，零距离对接就业。
平台具有项目实训管理板块，配备实训大纲、实训分组、实训评审、实训环境等多种功能模块，将项目全流程拆解为任务驱动模式，实现项目实战的便捷追踪与扁平化高效管理。
项目指导
U+人工智能实训平台，致力于培养人工智能产业应用型人才，让人工智能更加易教、易学、易应用，包含《人工智能导论》《Python数据处理》《机器学习》《神经网络与深度学习》《基于深度学习的自然语言处理》《数字图像处理》《计算机视觉》《智能语音识别》等体系化人工智能课程。现在加入AI助力计划，即可免费获取人工智能教学实验平台试用机会，还等什么呢~
后续，AI助力计划将持续推出更多优质的免费教学资源，关注公众号，及时获取更多AI干货！