Lie Group Machine Learning ebook

$106.00

This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers. Additional ISBNs 9783110499506Lie Group Machine Learning 1st Edition is written by Fanzhang Li; Li Zhang; Zhao Zhang and published by De Gruyter. ISBNs for Lie Group Machine Learning are 9783110498073, 3110498073 and the print ISBNs are 9783110500684, 311050068X. Additional ISBNs include 9783110499506.

Order Processing Time

All orders placed on our website are processed within 2-4 business days, from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Orders received after our daily cut-off time of 10:00 PM PT will be processed on the next business day. Please note that we do not process orders on weekends or public holidays.

Shipping Methods and Carriers

Zetlly partners exclusively with reputable shipping carriers to ensure timely delivery of your orders. We utilize:

FedEx
UPS
USPS

The choice of carrier is determined by factors such as destination, weight, and delivery timeframe to provide optimal service.

Shipping Rates and Fees

Free shipping is provided for all orders over $199.
Orders under $199 will incur a flat-rate shipping fee of $7.99.
All orders shipped within the United States will be subject to a sales tax charge of 5%.

Estimated Delivery Time

Once shipped, orders typically arrive within 6 to 10 business days. Our delivery times are from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Please allow additional time for deliveries to remote or rural locations.

Shipping Restrictions

Zetlly currently ships exclusively within the United States. At present, we do not offer international shipping or deliveries to P.O. boxes or APO/FPO addresses. Orders placed with addresses outside our designated delivery areas will be canceled, and refunds will be processed accordingly.

Tracking Your Order

Upon shipment, customers will receive a confirmation email containing tracking information. You can track your order directly through the provided tracking link or by visiting the carrier’s official website:

Please allow up to 48 hours for tracking information to update in the carrier’s system.

Eligibility for Returns and Exchanges

We accept returns and exchanges within 30 days from the date your order is delivered. Items must be unused, in the original condition, and accompanied by the original packaging and receipt or proof of purchase.

How to Return or Exchange an Item

To initiate a return or exchange, please follow these steps:

Contact our customer support at [email protected] with your order number and reason for return or exchange.
Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
Package your item securely and include all original packaging and proof of purchase.

Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA

Return Conditions

Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.

Return Shipping Costs

Customers are responsible for return shipping costs unless the return is due to our error or a defective product. We recommend using a trackable shipping service to ensure your return reaches us safely.

Non-Returnable Items

The following items cannot be returned:

Digital products (e-books or downloadable content)
Personalized or customized items
Gift cards

Accepted Payment Methods

Zetlly accepts the following secure and widely trusted payment options:

PayPal: Easily pay through your PayPal account, benefiting from secure transactions and buyer protection.
Stripe: Pay securely using major credit and debit cards including Visa, MasterCard, American Express, and Discover via Stripe’s encrypted payment gateway.

Payment Security

At Zetlly, your security is our utmost priority. We utilize advanced encryption technologies and robust security protocols provided by PayPal and Stripe. All payment information entered on our site is encrypted using Secure Socket Layer (SSL) technology, ensuring your financial information remains private and secure throughout the transaction process.

Zetlly does not store any credit card or sensitive financial information directly on our servers, further enhancing the security and protection of your personal data.

Payment Process and Confirmation

Upon placing an order, your chosen payment method (PayPal or Stripe) will immediately process the transaction. You will receive an automated confirmation email shortly after your payment has been successfully completed, detailing your transaction and order summary.

Please retain this confirmation email for your records and reference in case of any inquiries or disputes.

This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory.

Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers. Additional ISBNs 9783110499506Lie Group Machine Learning 1st Edition is written by Fanzhang Li; Li Zhang; Zhao Zhang and published by De Gruyter. ISBNs for Lie Group Machine Learning are 9783110498073, 3110498073 and the print ISBNs are 9783110500684, 311050068X. Additional ISBNs include 9783110499506.