Subspace learning of neural networks
Jian Cheng Lv
Reading Time
at 250 WPM4h 8m
The average reader, reading at a speed of 250 WPM, would take 4h 8m to read Subspace learning of neural networks.
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9
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248
total minutes
Subspace learning of neural networks
Published
2018
Publisher
Taylor & Francis Group
Pages
248
ISBN-13
9781351825320
Description
"Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors"--
Subjects
Frequently Asked Questions
How many pages are in Subspace learning of neural networks?
This edition of Subspace learning of neural networks has approximately 248 pages. Please note, this is an estimate and the exact page count can vary between hardcover, paperback, and e-book versions.
How long does it take to read Subspace learning of neural networks?
For most readers, Subspace learning of neural networks typically takes between 5h 10m and 3h 27m to complete. This is based on the book's length of approximately 62,000 words and common reading speeds.
Here's a detailed breakdown: • Continuous reading at 250 WPM: approximately 4h 8m of focused reading • Casual reading (30 minutes/day): you could finish in roughly 9 days • Estimated word count: 62,000 words
Your individual reading time will vary based on your personal reading pace, the amount of daily reading time, and your familiarity with the subject matter.
What is the word count of Subspace learning of neural networks?
The estimated word count for Subspace learning of neural networks is approximately 62,000 words. This figure is calculated using industry-standard methods that consider genre-specific word density patterns, typical formatting and layout characteristics, and standard words-per-page ratios for published books.
This is an approximation — actual word count may vary based on font size, formatting, edition, and the presence of illustrations or charts.
Who is the author of Subspace learning of neural networks?
Subspace learning of neural networks was written by Jian Cheng Lv.
When was Subspace learning of neural networks published?
The publication date for this specific edition is 2018. The original work may have been published on a different date.