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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.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Kanber, Burak. | |
| 245 | 1 | 0 | |a Hands-on Machine Learning with JavaScript : |b Solve complex computational web problems using machine learning. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (343 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 520 | |a 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 outliers; Transforming and normalizing data; Summary; Chapter 3: 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. | ||
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 500 | |a Tokenizing. | ||
| 520 | |a This book demonstrates various machine learning techniques and their implementation in JavaScript. Build models to power your applications with smart, predictive features. From predicting future prices, analyzing sentiments to medical diagnosis, this book shows you how to use the power of JavaScript to build efficient machine learning systems. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a JavaScript. | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a JavaScript (Computer program language) |2 fast | |
| 758 | |i has work: |a HANDS MACHINE LEARNING WITH JAVASCRIPT;SOLVE COMPLEX COMPUTATIONAL WEB PROBLEMS USING MACHINE LEARNING (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYY9XcvXBtWKqdTfQTrJcP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Kanber, Burak. |t Hands-on Machine Learning with JavaScript : Solve complex computational web problems using machine learning. |d Birmingham : Packt Publishing, ©2018 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5405677 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0036924760 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5405677 | ||
| 938 | |a EBSCOhost |b EBSC |n 1823653 | ||
| 994 | |a 92 |b IZTAP | ||