The Resource Applied Deep Learning with Keras, Bhagwat, Ritesh

Applied Deep Learning with Keras, Bhagwat, Ritesh

Label
Applied Deep Learning with Keras
Title
Applied Deep Learning with Keras
Statement of responsibility
Bhagwat, Ritesh
Creator
Contributor
Author
Subject
Genre
Language
  • eng
  • eng
Summary
Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API. Key Features Solve complex machine learning problems with precision Evaluate, tweak, and improve your deep learning models and solutions Use different types of neural networks to solve real-world problems Book Description Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code. Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You'll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you'll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you'll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you'll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model. By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks. What you will learn Understand the difference between single-layer and multi-layer neural network models Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks Apply L1, L2, and dropout regularization to improve the accuracy of your model Implement cross-validate using Keras wrappers with scikit-learn Understand the limitations of model accuracy Who this book is for If you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the mo..
http://library.link/vocab/creatorName
Bhagwat, Ritesh
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Abdolahnejad, Mahla
  • Moocarme, Matthew
  • O'Reilly Media Company
Label
Applied Deep Learning with Keras, Bhagwat, Ritesh
Link
https://databases.mvlc.org/connect/oreilly?ID=9781838555078
Instantiates
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Dimensions
unknown
Edition
1st edition
Extent
1 online resource (412 pages)
Form of item
online
Issuing body
Made available through: O'Reilly Media Company.
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Reproduction note
Electronic reproduction.
Specific material designation
remote
System control number
(CaSebORM)9781838555078
System details
Mode of access: World Wide Web
Label
Applied Deep Learning with Keras, Bhagwat, Ritesh
Link
https://databases.mvlc.org/connect/oreilly?ID=9781838555078
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Dimensions
unknown
Edition
1st edition
Extent
1 online resource (412 pages)
Form of item
online
Issuing body
Made available through: O'Reilly Media Company.
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Reproduction note
Electronic reproduction.
Specific material designation
remote
System control number
(CaSebORM)9781838555078
System details
Mode of access: World Wide Web

Library Locations

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      4 High Street, Suite 175, North Andover, MA, 01845, US
      42.7009413 -71.1255084
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