最新章節

• Summary
• Face recognition with PCA
• Visualizing high-dimensional data with PCA
• Performing PCA
• Eigenvectors and eigenvalues
• Variance covariance and covariance matrices

• Summary 更新時間：2021-07-02 19:01:34
• Face recognition with PCA
• Visualizing high-dimensional data with PCA
• Performing PCA
• Eigenvectors and eigenvalues
• Variance covariance and covariance matrices
• Principal component analysis
• Dimensionality Reduction with Principal Component Analysis
• Summary
• Clustering to learn features
• Image quantization
• Evaluating clusters
• Selecting K with the elbow method
• Local optima
• K-means
• Clustering
• K-means
• Summary
• Training a multi-layer perceptron to classify handwritten digits
• Training a multi-layer perceptron to approximate XOR
• Backpropagation
• Training multi-layer perceptrons
• Multi-layer perceptrons
• Feed-forward and feedback ANNs
• Nonlinear decision boundaries
• From the Perceptron to Artificial Neural Networks
• Summary
• Classifying characters in natural images
• Classifying handwritten digits
• Classifying characters in scikit-learn
• Maximum margin classification and support vectors
• Kernels and the kernel trick
• From the Perceptron to Support Vector Machines
• Summary
• Limitations of the perceptron
• Document classification with the perceptron
• Binary classification with the perceptron
• The perceptron learning algorithm
• Activation functions
• The perceptron
• The Perceptron
• Summary
• Stacking
• Boosting
• Bagging
• From Decision Trees to Random Forests and Other Ensemble Methods
• Summary
• Advantages and disadvantages of decision trees
• Decision trees with scikit-learn
• Gini impurity
• Information gain
• Selecting the questions
• Training decision trees
• Decision trees
• Nonlinear Classification and Regression with Decision Trees
• Summary
• Naive Bayes with scikit-learn
• Assumptions of Naive Bayes
• Naive Bayes
• Generative and discriminative models
• Bayes' theorem
• Naive Bayes
• Summary
• Multi-label classification performance metrics
• Multi-label classification and problem transformation
• Multi-class classification performance metrics
• Multi-class classification
• Tuning models with grid search
• ROC AUC
• Calculating the F1 measure
• Precision and recall
• Accuracy
• Binary classification performance metrics
• Spam filtering
• Binary classification with logistic regression
• From Linear Regression to Logistic Regression
• Summary
• Gradient descent
• Fitting and evaluating the model
• Exploring the data
• Applying linear regression
• Regularization
• Polynomial regression
• Multiple linear regression
• From Simple Linear Regression to Multiple Linear Regression
• Summary
• Using convolutional neural network activations as features
• Extracting features from pixel intensities
• Extracting features from images
• Word embeddings
• Space-efficient feature vectorizing with the hashing trick
• Extending bag-of-words with tf-idf weights
• Stemming and lemmatization
• Stop word filtering
• The bag-of-words model
• Extracting features from text
• Standardizing features
• Extracting features from categorical variables
• Feature Extraction
• Summary
• Scaling features
• Regression with KNN
• Classification with KNN
• Lazy learning and non-parametric models
• K-Nearest Neighbors
• Classification and Regression with k-Nearest Neighbors
• Summary
• Evaluating the model
• Solving OLS for simple linear regression
• Evaluating the fitness of the model with a cost function
• Simple linear regression
• Simple Linear Regression
• Summary
• Installing pandas Pillow NLTK and matplotlib
• Verifying the installation
• Installing Anaconda
• Installing on Mac OS
• Installing on Ubuntu 16.04
• Installing on Windows
• Installing using pip
• Installing scikit-learn
• An introduction to scikit-learn
• Bias and variance
• Training data testing data and validation data
• Machine learning tasks
• Learning from experience
• Defining machine learning
• The Fundamentals 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
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• 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
• The Fundamentals of Machine Learning
• Defining machine learning
• Learning from experience
• Machine learning tasks
• Training data testing data and validation data
• Bias and variance
• An introduction to scikit-learn
• Installing scikit-learn
• Installing using pip
• Installing on Windows
• Installing on Ubuntu 16.04
• Installing on Mac OS
• Installing Anaconda
• Verifying the installation
• Installing pandas Pillow NLTK and matplotlib
• Summary
• Simple Linear Regression
• Simple linear regression
• Evaluating the fitness of the model with a cost function
• Solving OLS for simple linear regression
• Evaluating the model
• Summary
• Classification and Regression with k-Nearest Neighbors
• K-Nearest Neighbors
• Lazy learning and non-parametric models
• Classification with KNN
• Regression with KNN
• Scaling features
• Summary
• Feature Extraction
• Extracting features from categorical variables
• Standardizing features
• Extracting features from text
• The bag-of-words model
• Stop word filtering
• Stemming and lemmatization
• Extending bag-of-words with tf-idf weights
• Space-efficient feature vectorizing with the hashing trick
• Word embeddings
• Extracting features from images
• Extracting features from pixel intensities
• Using convolutional neural network activations as features
• Summary
• From Simple Linear Regression to Multiple Linear Regression
• Multiple linear regression
• Polynomial regression
• Regularization
• Applying linear regression
• Exploring the data
• Fitting and evaluating the model
• Gradient descent
• Summary
• From Linear Regression to Logistic Regression
• Binary classification with logistic regression
• Spam filtering
• Binary classification performance metrics
• Accuracy
• Precision and recall
• Calculating the F1 measure
• ROC AUC
• Tuning models with grid search
• Multi-class classification
• Multi-class classification performance metrics
• Multi-label classification and problem transformation
• Multi-label classification performance metrics
• Summary
• Naive Bayes
• Bayes' theorem
• Generative and discriminative models
• Naive Bayes
• Assumptions of Naive Bayes
• Naive Bayes with scikit-learn
• Summary
• Nonlinear Classification and Regression with Decision Trees
• Decision trees
• Training decision trees
• Selecting the questions
• Information gain
• Gini impurity
• Decision trees with scikit-learn
• Advantages and disadvantages of decision trees
• Summary
• From Decision Trees to Random Forests and Other Ensemble Methods
• Bagging
• Boosting
• Stacking
• Summary
• The Perceptron
• The perceptron
• Activation functions
• The perceptron learning algorithm
• Binary classification with the perceptron
• Document classification with the perceptron
• Limitations of the perceptron
• Summary
• From the Perceptron to Support Vector Machines
• Kernels and the kernel trick
• Maximum margin classification and support vectors
• Classifying characters in scikit-learn
• Classifying handwritten digits
• Classifying characters in natural images
• Summary
• From the Perceptron to Artificial Neural Networks
• Nonlinear decision boundaries
• Feed-forward and feedback ANNs
• Multi-layer perceptrons
• Training multi-layer perceptrons
• Backpropagation
• Training a multi-layer perceptron to approximate XOR
• Training a multi-layer perceptron to classify handwritten digits
• Summary
• K-means
• Clustering
• K-means
• Local optima
• Selecting K with the elbow method
• Evaluating clusters
• Image quantization
• Clustering to learn features
• Summary
• Dimensionality Reduction with Principal Component Analysis
• Principal component analysis
• Variance covariance and covariance matrices
• Eigenvectors and eigenvalues
• Performing PCA
• Visualizing high-dimensional data with PCA
• Face recognition with PCA
• Summary 更新時間：2021-07-02 19:01:34