Name: Dimensionality Reduction in Machine Learning (Advanced Topics in Biomaterials) (en Inglés)
SKU: 63667333
Price: 192.62 USD
Availability: InStock
Author: Rad Jamal Amani,Chakraverty Snehashish,Parand Kourosh

portada Dimensionality Reduction in Machine Learning (Advanced Topics in Biomaterials) (en Inglés)

Dimensionality Reduction in Machine Learning (Advanced Topics in Biomaterials) (en Inglés)

Formato

Libro Físico

Editorial

Morgan Kaufmann

Autor

Rad Jamal Amani,Chakraverty Snehashish,Parand Kourosh

Idioma

Inglés

N° páginas

330

Encuadernación

Tapa Blanda

ISBN13

9780443328183

N° edición

Dimensionality Reduction in Machine Learning (Advanced Topics in Biomaterials) (en Inglés)

Rad Jamal Amani,Chakraverty Snehashish,Parand Kourosh (Autor) · Morgan Kaufmann · Tapa Blanda

Libro Nuevo Importado

Envío: 22 a 30 días háb.

$ 350.23$ 192.62

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 100 unidades

$ 192.62

Llega entre el 17 Sep y el 01 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Dimensionality Reduction in Machine Learning (Advanced Topics in Biomaterials) (en Inglés)"

Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding. Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.