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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-advanced-nlp/
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课程名称：深度学习：高级自然语言处理与递归神经网络（RNNs） 课程概述：你是否想过像OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等AI技术是如何工作的？在这门课程中，你将学习这些开创性应用的基础知识。自我发布第一门关于深度学习与自然语言处理（NLP）的课程以来，已经过去一年多，许多有趣的事情发生了，我深入学习和研究，积累了一些最有用的理念，以便反馈给你。 本课程的重点在于如何使用你所学的深度NLP基础知识，构建系统。你将能为文本分类（如情感分析和垃圾邮件检测）、神经机器翻译、问答系统等问题构建应用程序。我们将简要讨论聊天机器人，实际上，这类问题与机器翻译和问答系统没有什么不同。 为了解决这些问题，我们将探讨一些高级的深度NLP技术，如双向RNN、序列到序列（seq2seq）、注意力机制和记忆网络。课程材料均可免费下载并安装。大部分工作将在Python库（如Keras、Numpy、Tensorflow和Matplotlib）中完成，以便于学习高层次的概念。我会随时回答你的问题，帮助你在数据科学的旅程中前进。 本课程着重于“如何构建和理解”， 而不仅仅是“如何使用”。任何人只需15分钟阅读文档就能学会如何使用API。我们的目标是通过实验让你亲自看到模型内部发生了什么。如果你想深入了解机器学习模型，这门课程将非常适合你。 课程建议先修条件：具备良好的Python编码技能，了解RNN、CNN和词嵌入，能够使用Keras构建、训练和评估神经网络。 课程特点包括：每行代码详细解释，鼓励学生提出不同意见，不浪费时间在键盘上打字；不回避大学水平的数学，教授其他课程忽略的重要算法细节。 欢迎加入课堂！
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing).A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.So what is this course all about, and how have things changed since then?In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.This course takes you to a higher systems level of thinking.Since you know how these things work, it's time to build systems using these components.At the end of this course, you'll be able to build applications for problems like:text classification (examples are sentiment analysis and spam detection)neural machine translationquestion answeringWe'll take a brief look chatbots and as you'll learn in this course, this problem is actually no different from machine translation and question answering.To solve these problems, we're going to look at some advanced Deep NLP techniques, such as:bidirectional RNNsseq2seq (sequence-to-sequence)attentionmemory networksAll of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.See you in class!"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:Decent Python coding skillsUnderstand RNNs, CNNs, and word embeddingsKnow how to build, train, and evaluate a neural network in KerasWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
