最新章節(jié)

• Summary
• Where to go from here?
• Writing your own scikit-learn-based classifier in Python
• Writing your own OpenCV-based classifier in C++
• Building your own estimator
• Approaching a machine learning problem

• Summary 更新時(shí)間：2021-07-02 19:48:05
• Where to go from here?
• Writing your own scikit-learn-based classifier in Python
• Writing your own OpenCV-based classifier in C++
• Building your own estimator
• Approaching a machine learning problem
• Wrapping Up
• Summary
• Using pipelines in grid searches
• Implementing pipelines in scikit-learn
• Chaining algorithms together to form a pipeline
• Choosing the right regression metric
• Choosing the right classification metric
• Scoring models using different evaluation metrics
• Combining grid search with nested cross-validation
• Combining grid search with cross-validation
• Understanding the value of a validation set
• Implementing a simple grid search
• Tuning hyperparameters with grid search
• Implementing McNemar's test
• Implementing Student's t-test
• Assessing the significance of our results
• Manually implementing bootstrapping in OpenCV
• Estimating robustness using bootstrapping
• Implementing leave-one-out cross-validation
• Using scikit-learn for k-fold cross-validation
• Manually implementing cross-validation in OpenCV
• Understanding cross-validation
• Selecting the best model
• Evaluating a model in the right way
• Evaluating a model the wrong way
• Evaluating a model
• Selecting the Right Model with Hyperparameter Tuning
• Summary
• Implementing a voting classifier
• Understanding different voting schemes
• Combining different models into a voting classifier
• Implementing AdaBoost in scikit-learn
• Implementing AdaBoost in OpenCV
• Implementing AdaBoost
• Training and testing the random forest
• Preprocessing the dataset
• Loading the dataset
• Using random forests for face recognition
• Implementing extremely randomized trees
• Implementing a random forest with scikit-learn
• Implementing our first random forest
• Understanding the shortcomings of decision trees
• Combining decision trees into a random forest
• Understanding stacking ensembles
• Implementing a boosting regressor
• Implementing a boosting classifier
• Understanding boosting ensembles
• Implementing a bagging regressor
• Implementing a bagging classifier
• Understanding averaging ensembles
• Understanding ensemble methods
• Combining Different Algorithms into an Ensemble
• Summary
• Fitting the model
• Creating a convolutional neural network
• Preprocessing the MNIST dataset
• Training a deep neural net using Keras
• Training an MLP using OpenCV
• Preprocessing the MNIST dataset
• Loading the MNIST dataset
• Classifying handwritten digits
• Getting acquainted with Keras
• Getting acquainted with deep learning
• Training and testing the MLP classifier
• Customizing the MLP classifier
• Creating an MLP classifier in OpenCV
• Preprocessing the data
• Implementing a multilayer perceptron in OpenCV
• Training multi-layer perceptrons with backpropagation
• Understanding gradient descent
• Understanding multilayer perceptrons
• Applying the perceptron to data that is not linearly separable
• Evaluating the perceptron classifier
• Fitting the perceptron to data
• Generating a toy dataset
• Implementing your first perceptron
• Understanding the perceptron
• Understanding the McCulloch-Pitts neuron
• Using Deep Learning to Classify Handwritten Digits
• Summary
• Implementing agglomerative hierarchical clustering
• Understanding hierarchical clustering
• Organizing clusters as a hierarchical tree
• Running k-means
• Loading the dataset
• Classifying handwritten digits using k-means
• Reducing the color palette using k-means
• Visualizing the true-color palette
• Compressing color spaces using k-means
• Fourth caveat: k-means is slow for a large number of samples
• Third caveat: Cluster boundaries are linear
• Second caveat: We must select the number of clusters beforehand
• First caveat: No guarantee of finding the global optimum
• Knowing the limitations of expectation-maximization
• Implementing our own expectation-maximization solution
• Understanding expectation-maximization
• Implementing our first k-means example
• Understanding k-means clustering
• Understanding unsupervised learning
• Discovering Hidden Structures with Unsupervised Learning
• Summary
• Using tf-idf to improve the result
• Using n-grams to improve the result
• Training on the full dataset
• Training a normal Bayes classifier
• Preprocessing the data
• Building a data matrix using Pandas
• Loading the dataset
• Classifying emails using the naive Bayes classifier
• Visualizing conditional probabilities
• Classifying the data with a naive Bayes classifier
• Classifying the data with a normal Bayes classifier
• Creating a toy dataset
• Implementing your first Bayesian classifier
• Understanding the naive Bayes classifier
• Understanding Bayes' theorem
• Taking a short detour on probability theory
• Understanding Bayesian inference
• Implementing a Spam Filter with Bayesian Learning
• Summary
• Further improving the model
• Detecting pedestrians in a larger image
• Bootstrapping the model
• Implementing the support vector machine
• Generating negatives
• Taking a glimpse at the histogram of oriented gradients (HOG)
• Obtaining the dataset
• Detecting pedestrians in the wild
• Implementing nonlinear support vector machines
• Knowing our kernels
• Understanding the kernel trick
• Dealing with nonlinear decision boundaries
• Visualizing the decision boundary
• Building the support vector machine
• Preprocessing the dataset
• Visualizing the dataset
• Generating the dataset
• Implementing our first support vector machine
• Learning optimal decision boundaries
• Understanding linear support vector machines
• Detecting Pedestrians with Support Vector Machines
• Summary
• Using decision trees for regression
• Building the decision tree
• Loading the dataset
• Using decision trees to diagnose breast cancer
• Controlling the complexity of decision trees
• Understanding the decision rules
• Rating the importance of features
• Investigating the inner workings of a decision tree
• Visualizing a trained decision tree
• Constructing the tree
• Preprocessing the data
• Understanding the task by understanding the data
• Building our first decision tree
• Understanding decision trees
• Using Decision Trees to Make a Medical Diagnosis
• Summary
• Using Speeded Up Robust Features (SURF)
• Using the Scale-Invariant Feature Transform (SIFT)
• Detecting corners in images
• Encoding images in HSV and HLS space
• Encoding images in RGB space
• Using color spaces
• Representing images
• Representing text features
• Representing categorical variables
• Implementing Non-negative Matrix Factorization (NMF)
• Implementing Independent Component Analysis (ICA)
• Implementing Principal Component Analysis (PCA) in OpenCV
• Understanding dimensionality reduction
• Handling the missing data
• Binarizing features
• Scaling features to a range
• Normalizing features
• Standardizing features
• Preprocessing data
• Understanding feature engineering
• Representing Data and Engineering Features
• Summary
• Testing the classifier
• Training the classifier
• Splitting the data into training and test sets
• Inspecting the data
• Making it a binary classification problem
• Loading the training data
• Understanding logistic regression
• Classifying iris species using logistic regression
• Applying Lasso and ridge regression
• Testing the model
• Training the model
• Loading the dataset
• Using linear regression to predict Boston housing prices
• Understanding linear regression
• Using regression models to predict continuous outcomes
• Predicting the label of a new data point
• Training the classifier
• Generating the training data
• Implementing k-NN in OpenCV
• Understanding the k-NN algorithm
• Using classification models to predict class labels
• Scoring regressors using mean squared error explained variance and R squared
• Scoring classifiers using accuracy precision and recall
• Measuring model performance with scoring functions
• Having a look at supervised learning in OpenCV
• Understanding supervised learning
• First Steps in Supervised Learning
• Summary
• Dealing with data using OpenCV's TrainData container in C++
• Visualizing data from an external dataset
• Producing a simple plot
• Importing Matplotlib
• Visualizing the data using Matplotlib
• Loading external datasets in Python
• Creating multidimensional arrays
• Accessing single array elements by indexing
• Understanding NumPy arrays
• Importing NumPy
• Dealing with data using Python's NumPy package
• Starting a new IPython or Jupyter session
• Dealing with data using OpenCV and Python
• Understanding the machine learning workflow
• Working with Data in OpenCV and Python
• Summary
• Getting a glimpse of OpenCV's ML module
• Verifying the installation
• Installing OpenCV in a conda environment
• Getting to grips with Python's Anaconda distribution
• Getting the latest code for this book
• Installation
• Getting started with OpenCV
• Getting started with Python
• Problems that machine learning can solve
• Getting started with machine learning
• A Taste of Machine Learning
• Questions
• Piracy
• Errata
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Dedication
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewers
• About the Author
• Foreword
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• Foreword
• About the Author
• About the Reviewers
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Dedication
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Errata
• Piracy
• Questions
• 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
• 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
• 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
• 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
• Using the Scale-Invariant Feature Transform (SIFT)
• Using Speeded Up Robust Features (SURF)
• Summary
• Using Decision Trees to Make a Medical Diagnosis
• Understanding decision trees
• Building our first decision tree
• Understanding the task by understanding the data
• Preprocessing the data
• Constructing the tree
• Visualizing a trained decision tree
• Investigating the inner workings of a decision tree
• Rating the importance of features
• Understanding the decision rules
• Controlling the complexity of decision trees
• Using decision trees to diagnose breast cancer
• Loading the dataset
• Building the decision tree
• Using decision trees for regression
• Summary
• Detecting Pedestrians with Support Vector Machines
• Understanding linear support vector machines
• Learning optimal decision boundaries
• Implementing our first support vector machine
• Generating the dataset
• Visualizing the dataset
• Preprocessing the dataset
• Building the support vector machine
• Visualizing the decision boundary
• Dealing with nonlinear decision boundaries
• Understanding the kernel trick
• Knowing our kernels
• Implementing nonlinear support vector machines
• Detecting pedestrians in the wild
• Obtaining the dataset
• Taking a glimpse at the histogram of oriented gradients (HOG)
• Generating negatives
• Implementing the support vector machine
• Bootstrapping the model
• Detecting pedestrians in a larger image
• Further improving the model
• Summary
• Implementing a Spam Filter with Bayesian Learning
• Understanding Bayesian inference
• Taking a short detour on probability theory
• Understanding Bayes' theorem
• Understanding the naive Bayes classifier
• Implementing your first Bayesian classifier
• Creating a toy dataset
• Classifying the data with a normal Bayes classifier
• Classifying the data with a naive Bayes classifier
• Visualizing conditional probabilities
• Classifying emails using the naive Bayes classifier
• Loading the dataset
• Building a data matrix using Pandas
• Preprocessing the data
• Training a normal Bayes classifier
• Training on the full dataset
• Using n-grams to improve the result
• Using tf-idf to improve the result
• Summary
• Discovering Hidden Structures with Unsupervised Learning
• Understanding unsupervised learning
• Understanding k-means clustering
• Implementing our first k-means example
• Understanding expectation-maximization
• Implementing our own expectation-maximization solution
• Knowing the limitations of expectation-maximization
• First caveat: No guarantee of finding the global optimum
• Second caveat: We must select the number of clusters beforehand
• Third caveat: Cluster boundaries are linear
• Fourth caveat: k-means is slow for a large number of samples
• Compressing color spaces using k-means
• Visualizing the true-color palette
• Reducing the color palette using k-means
• Classifying handwritten digits using k-means
• Loading the dataset
• Running k-means
• Organizing clusters as a hierarchical tree
• Understanding hierarchical clustering
• Implementing agglomerative hierarchical clustering
• Summary
• Using Deep Learning to Classify Handwritten Digits
• Understanding the McCulloch-Pitts neuron
• Understanding the perceptron
• Implementing your first perceptron
• Generating a toy dataset
• Fitting the perceptron to data
• Evaluating the perceptron classifier
• Applying the perceptron to data that is not linearly separable
• Understanding multilayer perceptrons
• Understanding gradient descent
• Training multi-layer perceptrons with backpropagation
• Implementing a multilayer perceptron in OpenCV
• Preprocessing the data
• Creating an MLP classifier in OpenCV
• Customizing the MLP classifier
• Training and testing the MLP classifier
• Getting acquainted with deep learning
• Getting acquainted with Keras
• Classifying handwritten digits
• Loading the MNIST dataset
• Preprocessing the MNIST dataset
• Training an MLP using OpenCV
• Training a deep neural net using Keras
• Preprocessing the MNIST dataset
• Creating a convolutional neural network
• Fitting the model
• Summary
• Combining Different Algorithms into an Ensemble
• Understanding ensemble methods
• Understanding averaging ensembles
• Implementing a bagging classifier
• Implementing a bagging regressor
• Understanding boosting ensembles
• Implementing a boosting classifier
• Implementing a boosting regressor
• Understanding stacking ensembles
• Combining decision trees into a random forest
• Understanding the shortcomings of decision trees
• Implementing our first random forest
• Implementing a random forest with scikit-learn
• Implementing extremely randomized trees
• Using random forests for face recognition
• Loading the dataset
• Preprocessing the dataset
• Training and testing the random forest
• Implementing AdaBoost
• Implementing AdaBoost in OpenCV
• Implementing AdaBoost in scikit-learn
• Combining different models into a voting classifier
• Understanding different voting schemes
• Implementing a voting classifier
• Summary
• Selecting the Right Model with Hyperparameter Tuning
• Evaluating a model
• Evaluating a model the wrong way
• Evaluating a model in the right way
• Selecting the best model
• Understanding cross-validation
• Manually implementing cross-validation in OpenCV
• Using scikit-learn for k-fold cross-validation
• Implementing leave-one-out cross-validation
• Estimating robustness using bootstrapping
• Manually implementing bootstrapping in OpenCV
• Assessing the significance of our results
• Implementing Student's t-test
• Implementing McNemar's test
• Tuning hyperparameters with grid search
• Implementing a simple grid search
• Understanding the value of a validation set
• Combining grid search with cross-validation
• Combining grid search with nested cross-validation
• Scoring models using different evaluation metrics
• Choosing the right classification metric
• Choosing the right regression metric
• Chaining algorithms together to form a pipeline
• Implementing pipelines in scikit-learn
• Using pipelines in grid searches
• Summary
• Wrapping Up
• Approaching a machine learning problem
• Building your own estimator
• Writing your own OpenCV-based classifier in C++
• Writing your own scikit-learn-based classifier in Python
• Where to go from here?
• Summary 更新時(shí)間：2021-07-02 19:48:05