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Hands-on Machine Learning with JavaScript : Solve complex computational web problems using machine learning.
Chapter 2: 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 outlier...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: 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.
- ClusteringClassification; Regression; Dimensionality reduction; Optimization; Natural language processing; Image processing; Summary; Chapter 4: 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; Chapter 5: Classification Algorithms; k-Nearest Neighbor; Building the KNN algorithm.
- Example 1
- Height, weight, and genderExample 2
- Decolorizing a photo; Naive Bayes classifier; Tokenization; Building the algorithm; Example 3
- Movie review sentiment; Support Vector Machine; Random forest; Summary; Chapter 6: Association Rule Algorithms; The mathematical perspective; The algorithmic perspective; Association rule applications; Example
- retail data; Summary; Chapter 7: 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 techniquesFiltering; Seasonality analysis; Fourier analysis; Summary; Chapter 8: Artificial Neural Network Algorithms; Conceptual overview of neural networks; Backpropagation training; Example
- XOR in TensorFlow.js; Summary; Chapter 9: 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; Chapter 10: Natural Language Processing in Practice; String distance; Term frequency
- inverse document frequency.