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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Summary
• Combining models
• When it goes wrong
• Available resources

• Leave a review - let other readers know what you think 更新時間：2021-06-25 21:38:42
• Other Books You May Enjoy
• Summary
• Combining models
• When it goes wrong
• Available resources
• Format form input and output
• The task at hand
• Mode of learning
• Choosing the Best Algorithm for Your Application
• Summary
• Storing and delivering data
• Transformation and normalization
• Data joining and aggregation
• Data querying
• Data pipelines
• Continually improving and per-user models
• Web workers
• Training models on the server
• Serializing models
• Using Machine Learning in Real-Time Applications
• Summary
• Word embedding and neural networks
• Part of speech tagging
• Phonetics
• Stemming
• Tokenizing
• Term frequency - inverse document frequency
• String distance
• Natural Language Processing in Practice
• Summary
• Long Short-Term Memory
• Gated recurrent units
• SimpleRNN
• Recurrent neural networks
• Example – MNIST handwritten digits
• Convolutions and convolution layers
• Convolutional Neural Networks
• Deep Neural Networks
• Summary
• Example - XOR in TensorFlow.js
• Backpropagation training
• Conceptual overview of neural networks
• Artificial Neural Network Algorithms
• Summary
• Fourier analysis
• Seasonality analysis
• Filtering
• Other time-series analysis techniques
• Example 3 – polynomial regression
• Example 2 – exponential regression
• Example 1 – linear regression
• Regression basics
• Regression versus classification
• Forecasting with Regression Algorithms
• Summary
• Example – retail data
• Association rule applications
• The algorithmic perspective
• The mathematical perspective
• Association Rule Algorithms
• Summary
• Random forest
• Support Vector Machine
• Example 3 – Movie review sentiment
• Building the algorithm
• Tokenization
• Naive Bayes classifier
• Example 2 – Decolorizing a photo
• Example 1 – Height weight and gender
• Building the KNN algorithm
• k-Nearest Neighbor
• Classification Algorithms
• Summary
• k-means where k is unknown
• Example 2 – 3D data
• Example 1 – k-means on simple 2D data
• The main loop
• Updating centroid locations
• Assigning points to centroids
• Testing random centroid generation
• Initializing the algorithm
• Setting up the environment
• Writing the k-means algorithm
• Average and distance
• Grouping with Clustering Algorithms
• Summary
• Image processing
• Natural language processing
• Optimization
• Dimensionality reduction
• Regression
• Classification
• Clustering
• Categories of algorithms
• Reinforcement learning
• Supervised learning algorithms
• Measuring accuracy
• Supervised learning
• Unsupervised learning
• Types of learning
• Introduction to machine learning
• Tour of Machine Learning Algorithms
• Summary
• Transforming and normalizing data
• Handling outliers
• Handling noise
• Missing numerical data
• Missing categorical data
• Handling missing data
• Cleaning and preparing data
• Pearson correlation example
• Feature selection and feature extraction
• The curse of dimensionality
• Feature identification
• An overview
• Data Exploration
• Summary
• Creating a Hello World project
• Creating and initializing an example project
• Optionally installing Yarn
• Installing Node.js
• Preparing the development environment
• The async/await functions
• Promises
• The for...of function
• Object literals
• Arrow functions
• Module imports
• Classes
• Let and const
• Improvements in ES6
• TypeScript language
• Node.js
• The CommonJS initiative
• Advantages and challenges of JavaScript
• Why machine learning why now?
• Why JavaScript?
• Exploring the Potential of JavaScript
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewer
• About the author
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• 版權信息
• 封面

• 封面
• 版權信息
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the author
• About the reviewer
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• Exploring the Potential of JavaScript
• Why JavaScript?
• Why machine learning why now?
• Advantages and challenges of JavaScript
• The CommonJS initiative
• Node.js
• TypeScript language
• Improvements in ES6
• Let and const
• Classes
• Module imports
• Arrow functions
• Object literals
• The for...of function
• Promises
• The async/await functions
• Preparing the development environment
• Installing Node.js
• Optionally installing Yarn
• Creating and initializing an example project
• Creating a Hello World project
• Summary
• Data Exploration
• An overview
• Feature identification
• The curse of dimensionality
• Feature selection and feature extraction
• Pearson correlation example
• Cleaning and preparing data
• Handling missing data
• Missing categorical data
• Missing numerical data
• Handling noise
• Handling outliers
• Transforming and normalizing data
• Summary
• Tour of Machine Learning Algorithms
• Introduction to machine learning
• Types of learning
• Unsupervised learning
• Supervised learning
• Measuring accuracy
• Supervised learning algorithms
• Reinforcement learning
• Categories of algorithms
• Clustering
• Classification
• Regression
• Dimensionality reduction
• Optimization
• Natural language processing
• Image processing
• Summary
• Grouping with Clustering Algorithms
• Average and distance
• Writing the k-means algorithm
• Setting up the environment
• Initializing the algorithm
• Testing random centroid generation
• Assigning points to centroids
• Updating centroid locations
• The main loop
• Example 1 – k-means on simple 2D data
• Example 2 – 3D data
• k-means where k is unknown
• Summary
• Classification Algorithms
• k-Nearest Neighbor
• Building the KNN algorithm
• Example 1 – Height weight and gender
• Example 2 – Decolorizing a photo
• Naive Bayes classifier
• Tokenization
• Building the algorithm
• Example 3 – Movie review sentiment
• Support Vector Machine
• Random forest
• Summary
• Association Rule Algorithms
• The mathematical perspective
• The algorithmic perspective
• Association rule applications
• Example – retail data
• Summary
• Forecasting with Regression Algorithms
• Regression versus classification
• Regression basics
• Example 1 – linear regression
• Example 2 – exponential regression
• Example 3 – polynomial regression
• Other time-series analysis techniques
• Filtering
• Seasonality analysis
• Fourier analysis
• Summary
• Artificial Neural Network Algorithms
• Conceptual overview of neural networks
• Backpropagation training
• Example - XOR in TensorFlow.js
• Summary
• Deep Neural Networks
• Convolutional Neural Networks
• Convolutions and convolution layers
• Example – MNIST handwritten digits
• Recurrent neural networks
• SimpleRNN
• Gated recurrent units
• Long Short-Term Memory
• Summary
• Natural Language Processing in Practice
• String distance
• Term frequency - inverse document frequency
• Tokenizing
• Stemming
• Phonetics
• Part of speech tagging
• Word embedding and neural networks
• Summary
• Using Machine Learning in Real-Time Applications
• Serializing models
• Training models on the server
• Web workers
• Continually improving and per-user models
• Data pipelines
• Data querying
• Data joining and aggregation
• Transformation and normalization
• Storing and delivering data
• Summary
• Choosing the Best Algorithm for Your Application
• Mode of learning
• The task at hand
• Format form input and output
• Available resources
• When it goes wrong
• Combining models
• Summary
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-25 21:38:42