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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning-ai/
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课程名称：人工智能的线性代数 - 生成性人工智能 课程概述： 本课程旨在帮助您掌握线性代数，以促进数据科学、机器学习和深度学习的技能提升。随着对机器学习和深度学习领域专业知识的需求日益增长，理解其背后的数学原理至关重要。本课程提供了一个扎实的数学基础，帮助您深入领会算法的工作原理、局限性及其依赖的假设。 线性代数被广泛认可为这些领域学习旅程的基本起点。其基本元素——向量和矩阵，构成了存储和处理数据的基础，在线性回归、自然语言处理和深度学习等多种应用中发挥关键作用。即使在深度学习和神经网络等先进技术中，矩阵也用于存储图像和文本等输入，提供解决复杂问题的先进方案。 在本课程结束时，您将不仅能够理解线性代数的分析侧面，还将通过Python实践其应用。此外，您将深入了解著名的谷歌PageRank算法，并运用课程中学到的概念。 课程内容包括： - 向量基础 - 向量投影 - 向量基础 - 矩阵基础 - 矩阵变换 - 高斯消元法 - 爱因斯坦求和约定 - 特征问题 - 谷歌PageRank算法 - 奇异值分解（SVD） - 伪逆 - 矩阵分解 - 使用矩阵方法解决线性回归 - 从零开始的线性回归 - 自然语言处理中的线性代数 - 深度学习中的线性代数 - 使用PyTorch进行线性回归 - 优惠：Python基础及Python在数据科学中的应用 本课程采用实践式学习，将引导您逐步掌握数据科学、机器学习、自然语言处理和深度学习所需的线性代数技能。立即注册，开启您掌握人工智能应用数学基础的旅程！点击“注册”按钮，期待在第一节课上见到您！
Master Linear Algebra for Data Science, Machine Learning, and Deep Learning - Unleash the Power of Mathematics in AI ApplicationsAre you eager to enhance your skills in Machine Learning, Deep Learning, and Data Science by mastering the crucial foundation of Linear Algebra? Look no further - this comprehensive course is designed just for you.With the increasing demand for expertise in Machine Learning and Deep Learning, it's crucial to avoid the common mistake of relying solely on tools without a deep understanding of their underlying mathematical principles. This course is your key to developing a solid foundation in mathematics, providing you with a profound intuition of how algorithms work, their limitations, and the assumptions they rely on.Why is a strong mathematical foundation important? Understanding the machinery under the hood is the key to becoming a confident practitioner in the fields of Machine Learning, Data Science, and Deep Learning. Linear Algebra is universally acknowledged as a fundamental starting point in the learning journey of these domains.The basic elements of Linear Algebra - Vectors and Matrices - serve as the backbone for storing and processing data in various applications of Machine Learning, Data Science, and Artificial Intelligence. From basic operations to complex tasks involving massive datasets, Linear Algebra plays a pivotal role.Even in advanced technologies like Deep Learning and Neural Networks, Matrices are employed to store inputs such as images and text, providing state-of-the-art solutions to complex problems.Recognizing the paramount importance of Linear Algebra in a Data Science career, we have crafted a curriculum that ensures you build a strong intuition for the concepts without getting lost in complex mathematics.By the end of this course, you will not only grasp the analytical aspects of Linear Algebra but also witness its practical implementation through Python. Additionally, you will gain insights into the functioning of the renowned Google PageRank Algorithm, utilizing the concepts learned throughout the course.Here's what the course covers:Vectors BasicsVector ProjectionsBasis of VectorsMatrices BasicsMatrix TransformationsGaussian EliminationEinstein Summation ConventionEigen ProblemsGoogle Page Rank AlgorithmSVD - Singular Value DecompositionPseudo InverseMatrix DecompositionSolve Linear Regression using Matrix MethodsLinear Regression from ScratchLinear Algebra in Natural Language ProcessingLinear Algebra for Deep LearningLinear Regression using PyTorchBonus: Python Basics & Python for Data ScienceThis hands-on course takes you on a step-by-step journey, providing the essential Linear Algebra skills required for Data Science, Machine Learning, Natural Language Processing, and Deep Learning. Enroll now and embark on your journey to master the mathematical foundations powering AI applications. Click the 'Enroll' button to start your learning experience - I look forward to seeing you in Lecture 1!
