Deep Learning Generalization
Liu Peng
Sold by Rarewaves USA, OSWEGO, IL, U.S.A.
AbeBooks Seller since 10 June 2025
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by Rarewaves USA, OSWEGO, IL, U.S.A.
AbeBooks Seller since 10 June 2025
Condition: New
Quantity: 1 available
Add to basketThis book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization.The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized deep learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes.By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications.For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in deep learning.
Seller Inventory # LU-9781032841892
This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization.
The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized deep learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes.
By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications.
For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in deep learning.
Liu Peng is currently an Assistant Professor of Quantitative Finance at the Singapore Management University (SMU). His research interests include generalization in deep learning, sparse estimation, Bayesian optimization.
"About this title" may belong to another edition of this title.
If you are a consumer you can cancel the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.
INFORMATION REGARDING THE RIGHT OF CANCELLATION
Statutory Right to cancel
You have the right to cancel this contract within 14 days for any reason.
The cancellation period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the the last good or the last lot or piece.
To exercise the right to cancel, you must inform us, Rarewaves USA, 10100 W Sample Rd, Ste 101, 33065, Coral Springs, Florida, U.S.A., of your decision to cancel this contract by a clear statement (e.g. a letter sent by post, fax or e-mail). You may use the attached model cancellation form, but it is not obligatory. You can also electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". If you use this option, we will communicate to you an acknowledgement of receipt of such a cancellation on a durable medium (e.g. by e-mail) without delay.
To meet the cancellation deadline, it is sufficient for you to send your communication concerning your exercise of the right to cancel before the cancellation period has expired.
Effects of cancellation
If you cancel this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).
We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.
We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to cancel with contract.
We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.
We may withhold reimbursement until we have received the goods back or you have supplied evidence of having sent back the goods, whichever is the earliest.
You shall send back the goods or hand them over to us or Rarewaves USA, 10100 W Sample Rd, Ste 101, 33065, Coral Springs, Florida, U.S.A., without undue delay and in any event not later than 14 days from the day on which you communicate your cancellation from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.
Exceptions to the right of cancellation
The right of cancellation does not apply to:
Model withdrawal form
(complete and return this form only if you wish to withdraw from the contract)
To: (Rarewaves USA, 10100 W Sample Rd, Ste 101, 33065, Coral Springs, Florida, U.S.A.)
I/We (*) hereby give notice that I/We (*) withdraw from my/our (*) contract of sale of the following goods (*)/for the provision of the following goods (*)/for the provision of the following service (*),
Ordered on (*)/received on (*)
Name of consumer(s)
Address of consumer(s)
Signature of consumer(s) (only if this form is notified on paper)
Date
* Delete as appropriate.
Please note that we do not offer Priority shipping to any country.
We currently do not ship to the below countries:
Afghanistan
Bhutan
Brazil
Brunei Darussalam
Channel Islands
Chile
Israel
Lao
Mexico
Russian Federation
Saudi Arabia
South Africa
Yemen
Please do not attempt to place orders with any of these countries as a ship to address - they will be cancelled.
| Order quantity | 6 to 9 business days | 6 to 9 business days |
|---|---|---|
| First item | £ 0.00 | £ 0.00 |
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.