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The Regularization Cookbook: Explore practical recipes to improve the functional
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N.º de artículo de eBay:388463522310
Última actualización el 14 oct 2025 03:11:53 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- ISBN-13
- 9781837634088
- Book Title
- The Regularization Cookbook
- ISBN
- 9781837634088
Acerca de este producto
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1837634084
ISBN-13
9781837634088
eBay Product ID (ePID)
8062635346
Product Key Features
Number of Pages
424 Pages
Language
English
Publication Name
Regularization Cookbook : Explore Practical Recipes to Improve the Functionality of Your ML Models
Publication Year
2023
Subject
Applied
Type
Textbook
Subject Area
Mathematics
Format
Trade Paperback
Dimensions
Item Height
0.9 in
Item Weight
25.6 Oz
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Intended Audience
Trade
TitleLeading
The
Synopsis
Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBook Key Features: Learn to diagnose the need for regularization in any machine learning model Regularize different ML models using a variety of techniques and methods Enhance the functionality of your models using state of the art computer vision and NLP techniques Book Description: Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you'll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you'll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you'll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E. By the end of this book, you'll be armed with different regularization techniques to apply to your ML and DL models. What You Will Learn: Diagnose overfitting and the need for regularization Regularize common linear models such as logistic regression Understand regularizing tree-based models such as XGBoos Uncover the secrets of structured data to regularize ML models Explore general techniques to regularize deep learning models Discover specific regularization techniques for NLP problems using transformers Understand the regularization in computer vision models and CNN architectures Apply cutting-edge computer vision regularization with generative models Who this book is for: This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite., Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to diagnose the need for regularization in any machine learning model Regularize different ML models using a variety of techniques and methods Enhance the functionality of your models using state of the art computer vision and NLP techniques Book Description Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you'll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you'll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you'll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E.By the end of this book, you'll be armed with different regularization techniques to apply to your ML and DL models. What you will learn Diagnose overfitting and the need for regularization Regularize common linear models such as logistic regression Understand regularizing tree-based models such as XGBoos Uncover the secrets of structured data to regularize ML models Explore general techniques to regularize deep learning models Discover specific regularization techniques for NLP problems using transformers Understand the regularization in computer vision models and CNN architectures Apply cutting-edge computer vision regularization with generative models Who this book is for This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite. ]]>
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grandeagleretail
98,7% de votos positivos•2,8 millones artículos vendidos
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Votos de vendedor (1.068.789)
- c***a (121)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaThe seller was very responsive and answered me on a timely matter. The product itself came in its packaging and was new, not used at all. The packaging was not beat up or anything, safely delivered to my mailbox. No mix ups and zero stress with delivery. The price for the product is completely understandable for the product. I really appreciate the seller and I am very happy to have purchased through this seller. Completely trustable!
- m***4 (1614)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaLeaving positive feedback because 1) item was packed well & arrived as described 2) seller did give partial refund when subsequent price dropped below org purchase price. 3) communication was quick However, there was a downside to this transaction -item listed as in-stock but ended up waiting nearly a month for them to get it from their distributer then ship it to me (bought June 29th, arrived around July 21). Auction said 12-15 days. Better clarity would have prevented lot of frustrationStreet Fighter II - Guile (Player 2) Figure - 6 Inch (#365698737394)
- n***i (4)- Votos emitidos por el comprador.Mes pasadoCompra verificadaMy statue was exactly as described, it was un-opened and in perfect condition! Totally wirth the price. Getting it shipped took a little long but the seller was very responsive when I messaged and sent me the tracking as soon as it was available. Plus they did a good job packaging it well enough that despite being banged up on the outside my item was completely undamaged.