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所在平台: Udemy |
课程主页: https://www.udemy.com/course/natural-language-processing-from-first-principles/
课程评论：没有评论
**课程名称：** 自然语言处理从基本原理出发 (Natural Language Processing From First Principles) **课程概述：** 本课程面向有动力的初学者，教授自然语言处理（NLP）和深度学习的基础知识。学员将仅使用Numpy和少量微积分，从零开始编写自己的词嵌入向量。 课程包含一个针对背景不足的学员的数学速成课，简明扼要地回顾了微分演算和线性代数的基本概念，确保学员能轻松理解所有数学推导。我们强调所有数学推导的每一步，旨在帮助学员深入理解自然语言处理乃至人工智能。 课程不只是提供现成的结果，而是教授学员如何直接从原始资料中获取信息。我将展示一个可复制且易于记忆的框架，帮助你阅读、理解并实现深度学习研究论文。你将洞悉研究论文中的术语如何映射到实际代码，这将是人工智能和数据科学从业者的必备技能，助你脱颖而出。 课程贯穿始终强调良好的编码实践。学员将从一开始就学习编写Pythonic且可扩展代码的基础，以便轻松过渡到编写更复杂的生产代码。 **课程结束时，学员将能够回答以下问题：** * Skip-gram模型和连续词袋模型（Continuous Bag of Words）之间有什么区别？ * 什么是分布语义学（distributional semantics）？ * 如何使用向量教会计算机理解语言？ * 如何推导出Word2Vec的梯度？ * 为什么Softmax函数在自然语言处理中速度较慢？ * 如何处理自然语言处理中的小型数据集？ * 如何使用负采样（negative sampling）来改进词嵌入？ * 在自然语言处理中处理专有名词的最佳方法是什么？ * 自然语言处理的历史方法有哪些？ * 词语图（word plots）能教会我们什么关于计算机如何理解语言？ 本课程内容充实，节奏明快，适合希望深入了解自然语言处理的有动力的初学者。完成本课程后，你将学会独立实现研究论文，告别对Medium博客的依赖。 **教学大纲：** 无
In this course motivated beginners will learn the fundamentals of natural language processing and deep learning. Students will code their own word embedding vectors from scratch, using just Numpy and a little bit of calculus. For students who don't have the required background, a crash course in the required mathematics is included. We'll cover the fundamentals of differential calculus and linear algebra in a succinct overview, so students can easily follow all mathematical derivations. Rather than simply be presented with results, each step of the mathematical derivations is included. This is to help students foster a deeper understanding of natural language processing and artificial intelligence in general.Far from being a course where students are simply spoon fed the instructors' interpretation, students will learn to gather information directly from the source. I will show you a repeatable and easy to remember framework to read, understand, and implement deep learning research papers. You will get insight into how the verbiage in research papers maps to real world code. This is an essential skill set for all practitioners of artificial intelligence and data science, and will help you stand out from the crowd. Throughout the course, good coding practices will be stressed. Students will learn the fundamentals of writing pythonic and extensible code from the very beginning, so that they can easily transition into writing more complex code for production.By the end of the course, students will be able to answer the following questions:What is the difference between the skip-gram and continuous bag of words models?What is distributional semantics?How can we use vectors to teach computers about language?How do we derive the word2vec gradients?Why is the softmax function so slow in natural language processing?How can we deal with small datasets for natural language processing?How can we improve word embedding using negative sampling?What is the best way to to deal with proper nouns in natural language processing?What were some of the historical approaches to natural language processing?What can word plots teach us about how computers understand language?There is zero fluff in this course. It is taught at a brisk pace, and is intended for motivated beginners who want deeper insights into natural language processing. Those that complete this course will learn how to implement research papers on there own; you'll never have to rely on Medium blog posts again.
