Descriptions
Dimensionality Reduction in Machine Learning, Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various real-world contexts. The book provides a comprehensive overview of key techniques such as Principal Component Analysis (PCA), t-SNE, autoencoders, and manifold learning, explaining their theoretical foundations and practical implementation. Readers will learn how to select and apply the right dimensionality reduction method for different types of data, improve model performance, and visualize high-dimensional datasets. This resource is ideal for students, researchers, and practitioners seeking to master dimensionality reduction for machine learning and data science applications.
Book features
- Understand the mathematical foundations and programming aspects of dimensionality reduction algorithms.
- Compare and implement key techniques such as PCA, t-SNE, autoencoders, and manifold learning.
- Select appropriate dimensionality reduction methods for different data types and tasks.
- Improve machine learning model performance by reducing data dimensionality.
- Visualize and interpret high-dimensional datasets effectively.
Who this Book is for
- Students studying machine learning, data science, or artificial intelligence.
- Researchers seeking a comprehensive reference on dimensionality reduction.
- Data scientists and practitioners applying dimensionality reduction in real-world projects.
- Anyone interested in understanding and visualizing high-dimensional data.
Specificatoin of Dimensionality Reduction in Machine Learning
- Publisher : Elsevier
- Teacher : Jamal Amani Rad
- Language : English
- Level : All Levels
- Pages: 314
- Chapters: 11
- Format: PDF
Content of Dimensionality Reduction in Machine Learning
Pictures
Installation Guide
Extract the files and watch with your favorite Software
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
13 MB
