Elementary_probability_theory.pdf

This book provides an introduction to probability theory and its applications. The emphasis is on essential probabilistic reasoning, which is illustrated with a large number of samples. The fourth edition adds material related to mathematical finance as well as expansions on stable laws and martingales.

Statistics in MATLAB_ A Primer.pdf

Statistics in MATLAB: A Primer provides a comprehensive introduction to MATLAB and its extensive functionality for statistics. Topics include descriptive statistics, regression analysis, visualization, hypothesis testing, and machine learning. MATLAB and Statistics and Machine Learning Toolbox are used to solve examples in the book.

Machine Learning for OpenCV_ Intelligent image processing with Python.pdf

Chapter 1: A Taste of Machine Learning Chapter 2: Working with Data in OpenCV and Python Chapter 3: First Steps in Supervised Learning Chapter 4: Representing Data and Engineering Features Chapter 5: Using Decision Trees to Make a Medical Diagnosis Chapter 6: Detecting Pedestrians with Support Vector Machines Chapter 7: Implementing a Spam Filter with Bayesian Learning Chapter 8: Discovering Hidden Structures with Unsupervised Learning Chapter 9: Using Deep Learning to Classify Handwritten Digits Chapter 10: Combining Different Algorithms into an Ensemble Chapter 11: Selecting the Right Model with Hyperparameter Tuning Chapter 12: Wrapping Up

Hands-On Start to Wolfram Mathematica.pdf

WOLFRAM MATHEMATICA 实用编程指南 Part I The Complete Overview Chapter 1 The Very Basics Chapter 2 A Sample Project in Mathematica Chapter 3 Input and Output Chapter 4 Word Processing and Typesetting Chapter 5 Presenting with Slide Shows Chapter 6 Fundamentals of the Wolfram Language Chapter 7 Creating Interactive Models with a Single Command Chapter 8 Sharing Mathematica Notebooks Chapter 9 Finding Help Part II Extending Knowledge Chapter 10 2D and 3D Graphics Chapter 11 Visualizing Data Chapter 12 Styling and Customizing Graphics Chapter 13 Creating Figures and Diagrams with Graphics Primitives Chapter 14 Algebraic Manipulation and Equation Solving Chapter 15 Calculus Chapter 16 Differential Equations Chapter 17 Linear Algebra Chapter 18 Probability and Statistics Chapter 19 Importing and Exporting Data Chapter 20 Data Filtering and Manipulation Chapter 21 Working with Curated Data Chapter 22 Using Wolfram|Alpha Data in Mathematica Chapter 23 Statistical Functionality for Data Analysis Chapter 24 Creating Programs Chapter 25 Creating Parallel and GPU Programs Index

Advanced PID Control-ISA (2005).pdf

作者Karl J. Astrom, Tore Hagglund，英文版。With a total of 13 chapters, this new book substantially expands on some of the topics covered in the previous versions and provides several new chapters that deal with controller design, feedforward design, replacement of the Ziegler-Nichols tuning rules, predictive control, loop and performance assessment and interaction. At this point in our book trilogy, we assume that the reader is highly familiar with control theory.

Data Structures and Algorithms in C++

C++描述的数据结构与算法，全英文。关于源码： The source code for the text example programs is available for download at cengagebrain.com and via the author’s Web site at http://www.mathcs.duq. edu/drozdek/DSinCpp.

Getting Started with Python and Raspberry Pi

Learn to design and implement reliable Python applications on the Raspberry Pi using a range of external libraries, the Raspberry Pis GPIO port, and the camera module