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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mathematics-behind-large-language-models-and-transformers/
课程评论：没有评论
课程名称：大型语言模型和变压器背后的数学 课程概述：欢迎来到变压器的数学课程，这是为那些渴望理解大型语言模型（如GPT、BERT等）数学基础而精心设计的深入课程。本课程深入探讨了使这些复杂模型能够处理、理解和生成类似人类文本的数学算法。课程开始于文本的标记化，学生将学习如何通过WordPiece算法等技术将原始文本转换为模型可理解的格式。 我们将探讨变压器架构的核心组件——关键矩阵、查询矩阵和值矩阵，以及它们在信息编码中的作用。课程将重点关注注意力机制的原理，包括对多头注意力和注意力掩码的详细研究。这些概念对于使模型能够关注输入数据中相关部分至关重要，从而增强其理解上下文和细微差别的能力。 此外，我们还将涵盖位置编码，这对于维持输入中单词的顺序至关重要，利用余弦和正弦函数以数学方式嵌入位置信息。本课程还包括对双向和掩码语言模型、向量、点积和多维词嵌入的全面见解，这些对创建单词的密集表示至关重要。 到本课程结束时，参与者不仅将掌握变压器的理论基础，还将获得关于其功能和应用的实际见解。这些知识将帮助你在机器学习领域中创新并脱颖而出，使你成为人工智能工程师和研究人员中的佼佼者。
Welcome to the Mathematics of Transformers, an in-depth course crafted for those eager to understand the mathematical foundations of large language models like GPT, BERT, and beyond. This course delves into the complex mathematical algorithms that allow these sophisticated models to process, understand, and generate human-like text. Starting with tokenization, students will learn how raw text is converted into a format understandable by models through techniques such as the WordPiece algorithm. We'll explore the core components of transformer architectures-key matrices, query matrices, and value matrices-and their roles in encoding information. A significant focus will be on the mechanics of the attention mechanism, including detailed studies of multi-head attention and attention masks. These concepts are pivotal in enabling models to focus on relevant parts of the input data, enhancing their ability to understand context and nuance. We will also cover positional encodings, essential for maintaining the sequence of words in inputs, utilizing cosine and sine functions to embed the position information mathematically. Additionally, the course will include comprehensive insights into bidirectional and masked language models, vectors, dot products, and multi-dimensional word embeddings, crucial for creating dense representations of words. By the end of this course, participants will not only master the theoretical underpinnings of transformers but also gain practical insights into their functionality and application. This knowledge will prepare you to innovate and excel in the field of machine learning, placing you among the top echelons of AI engineers and researchers
