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OpenCV機器學習(影印版)(簡體書)
  • OpenCV機器學習(影印版)(簡體書)

  • ISBN13:9787564183240
  • 出版社:東南大學出版社
  • 作者:Michael Beyeler
  • 裝訂/頁數:平裝/357頁
  • 規格:26cm*19cm (高/寬)
  • 出版日:2019/05/01
  • 促銷優惠:新書優惠
人民幣定價:96元
定  價:NT$576元
優惠價: 79455
可得紅利積點:13 點

庫存: 無庫存,下單後進貨(採購期約45個工作天)

商品簡介

作者簡介

目次

OpenCV庫包含500多個函數,2500多種算法,可以擴展應用到視覺行業中的很多領域,有廣泛的用途,比如安保,醫學成像,模式與人臉識別,機器人和工業產品檢測,等等。本書實用性強,內容全面,講解透徹,可以幫助讀者掌握如何構建具有一定AI(人工智能)的應用程序,使計算機能夠“看見”並根據所得到的數據來做出決策。
邁克爾·貝耶勒是華盛頓大學神經工程和數據科學專業的博士後,主攻仿生視覺計算模型,用以為盲人植入人工視網膜(仿生眼睛),改善盲人的視覺體驗。他的工作屬￿神經科學、計算機工程、計算機視覺和機器學習的交叉領域。他也是2015年Packt出版的《OpenCV with Python Blueprints》一書的作者,該書是構建高級計算機視覺項目的實用指南。同時他也是多個開源項目的積極貢獻者,具有Python、C/C++、CUDA、MATLAB和Android的專業編程經驗。他還擁有加利福尼亞大學歐文分校計算機科學專業的博士學位、瑞士蘇黎世聯邦理工學院生物醫學專業的碩士學位和電子工程專業的學士學位。當他不“呆頭呆腦”地研究大腦時,他會攀登雪山、參加現場音樂會或者彈鋼琴。
Preface
Chapter 1:A Taste of Machine Learning
Getting started with machine learning
Problems that machine learning can solve
Getting started with Python
Getting started with OpenCV
Installation
Getting the latest code for this book
Getting to grips with Python's Anaconda distribution
Installing OpenCV in a conda environment
Verifying the installation
Getting a glimpse of OpenCV's ML module
Summary
Chapter 2: Working with Data in OpenCV and Python
Understanding the machine learning workflow
Dealing with data using OpenCV and Python
Starting a new IPython or Jupyter session
Dealing with data using Python's NumPy package
Importing NumPy
Understanding NumPy arrays
Accessing single array elements by indexing
Creating multidimensional arrays
Loading external datasets in Python
Visualizing the data using Matplotlib
Importing Matplotlib
Producing a simple plot
Visualizing data from an external dataset
Dealing with data using OpenCV's TrainData container in C++
Summary
Chapter 3: First Steps in Supervised Learning
Understanding supervised learning
Having a look at supervised learning in OpenCV
Measuring model performance with scoring functions
Scoring classifiers using accuracy, precision, and recall
Scoring regressors using mean squared error, explained variance, and R squared
Using classification models to predict class labels
Understanding the k-NN algorithm
Implementing k-NN in OpenCV
Generating the training data
Training the classifier
Predicting the label of a new data point
Using regression models to predict continuous outcomes
Understanding linear regression
Using linear regression to predict Boston housing prices
Loading the dataset
Training the model
Testing the model
Applying Lasso and ridge regression
Classifying iris species using logistic regression
Understanding logistic regression
Loading the training data
Making it a binary classification problem
Inspecting the data
Splitting the data into training and test sets
Training the classifier
Testing the classifier
Summary
Chapter 4: Representing Data and Engineering Features
Understanding feature engineering
Preprocessing data
Standardizing features
Normalizing features
Scaling features to a range
Binarizing features
Handling the missing data
Understanding dimensionality reduction
Implementing Principal Component Analysis (PCA) in OpenCV
Implementing Independent Component Analysis (ICA)
Implementing Non-negative Matrix Factorization (NMF)
Representing categorical variables
Representing text features
Representing images
Using color spaces
Encoding images in RGB space
Encoding images in HSV and HLS space
Detecting corners in images
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 Deed Learning to Classifv Handwritten Diqits
Chapter 10: Combining Different Algorithms into an Ensemble
Chapter 11:Selecting the Right Model with Hyperparameter Tuning
Chapter 12: Wrapping Up

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