最优化教材，PCL里面BFGS的实现就是按照这本书。里面优化的理论在机器学习中也广泛应用

Numerical Methods for Unconstrained Optimization and Nonlinear Eauations.介绍了newton method,broyden method等诸多方法求解无约束求解非线性最小二乘问题.

Numerical Methods for Chemical Engineering Applications in MATLAB.pdf

单传感器相机经典书籍。

数字图像处理的书籍，希望能帮助大家，谢谢。

Author: Brain D. O. Anderson and John B. Moore

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills. The book covers a wide range of topics―from numerical linear algebra to optimization and differential equations―focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material. The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background. Table of Contents Section I: Preliminaries Chapter 1: Mathematics Review Chapter 2: Numerics and Error Analysis Section II: Linear Algebra Chapter 3: Linear Systems and the LU Decomposition Chapter 4: Designing and Analyzing Linear Systems Chapter 5: Column Spaces and QR Chapter 6: Eigenvectors Chapter 7: Singular Value Decomposition Section III: Nonlinear Techniques Chapter 8: Nonlinear Systems Chapter 9: Unconstrained Optimization Chapter 10: Constrained Optimization Chapter 11: Iterative Linear Solvers Chapter 12: Specialized Optimization Methods Section IV: Functions, Derivatives, and Integrals Chapter 13: Interpolation Chapter 14: Integration and Differentiation Chapter 15: Ordinary Differential Equations Chapter 16: Partial Differential Equations

英文原版书籍，介绍信息系统分析与设计的原理，重点探讨系统开发生命周期的前期和中期活动，即系统分析和设计活动。

谱方法的matlab教程，内有详细的推导与matlab相应代码。

清晰 彩色 When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search.