The Resource Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil
Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil
Resource Information
The item Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
- Summary
- Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. You will: Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models
- Language
- eng
- Edition
- Second edition.
- Extent
- 1 online resource (xvii, 306 pages)
- Note
- Includes index
- Contents
-
- Chapter 1 - Introduction Deep Learning
- Chapter 2 - Introduction to PyTorch
- Chapter 3- Feed Forward Networks
- Chapter 4 - Automatic Differentiation in Deep Learning
- Chapter 5 - Training Deep Neural Networks
- Chapter 6 - Convolutional Neural Networks
- Chapter 7 - Recurrent Neural Networks
- Chapter 8 - Recent advances in Deep Learning
- Isbn
- 9781484253649
- Label
- Deep learning with Python : learn best practices of deep learning models with PyTorch
- Title
- Deep learning with Python
- Title remainder
- learn best practices of deep learning models with PyTorch
- Statement of responsibility
- Nikhil Ketkar, Jojo Moolayil
- Language
- eng
- Summary
- Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. You will: Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models
- Cataloging source
- GW5XE
- http://library.link/vocab/creatorName
- Ketkar, Nikhil
- Dewey number
- 005.13/3
- Illustrations
- illustrations
- Index
- index present
- LC call number
- QA76.73.P98
- Literary form
- non fiction
- Nature of contents
- dictionaries
- http://library.link/vocab/relatedWorkOrContributorName
- Moolayil, Jojo
- http://library.link/vocab/subjectName
-
- Machine learning
- Python (Computer program language)
- Data mining
- Data mining
- Machine learning
- Python (Computer program language)
- Label
- Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil
- Note
- Includes index
- Antecedent source
- unknown
- 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
- Contents
- Chapter 1 - Introduction Deep Learning -- Chapter 2 - Introduction to PyTorch -- Chapter 3- Feed Forward Networks -- Chapter 4 - Automatic Differentiation in Deep Learning -- Chapter 5 - Training Deep Neural Networks -- Chapter 6 - Convolutional Neural Networks -- Chapter 7 - Recurrent Neural Networks -- Chapter 8 - Recent advances in Deep Learning
- Control code
- 1246247219
- Dimensions
- unknown
- Edition
- Second edition.
- Extent
- 1 online resource (xvii, 306 pages)
- File format
- unknown
- Form of item
- online
- Isbn
- 9781484253649
- Level of compression
- unknown
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-4842-5364-9
- Other physical details
- illustrations
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Sound
- unknown sound
- Specific material designation
- remote
- System control number
- (OCoLC)1246247219
- Label
- Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil
- Note
- Includes index
- Antecedent source
- unknown
- 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
- Contents
- Chapter 1 - Introduction Deep Learning -- Chapter 2 - Introduction to PyTorch -- Chapter 3- Feed Forward Networks -- Chapter 4 - Automatic Differentiation in Deep Learning -- Chapter 5 - Training Deep Neural Networks -- Chapter 6 - Convolutional Neural Networks -- Chapter 7 - Recurrent Neural Networks -- Chapter 8 - Recent advances in Deep Learning
- Control code
- 1246247219
- Dimensions
- unknown
- Edition
- Second edition.
- Extent
- 1 online resource (xvii, 306 pages)
- File format
- unknown
- Form of item
- online
- Isbn
- 9781484253649
- Level of compression
- unknown
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-4842-5364-9
- Other physical details
- illustrations
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Sound
- unknown sound
- Specific material designation
- remote
- System control number
- (OCoLC)1246247219
Library Links
Embed
Settings
Select options that apply then copy and paste the RDF/HTML data fragment to include in your application
Embed this data in a secure (HTTPS) page:
Layout options:
Include data citation:
<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Deep-learning-with-Python--learn-best-practices/KF8Q2PvKmhw/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Deep-learning-with-Python--learn-best-practices/KF8Q2PvKmhw/">Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements
Preview
Cite Data - Experimental
Data Citation of the Item Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil
Copy and paste the following RDF/HTML data fragment to cite this resource
<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Deep-learning-with-Python--learn-best-practices/KF8Q2PvKmhw/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Deep-learning-with-Python--learn-best-practices/KF8Q2PvKmhw/">Deep learning with Python : learn best practices of deep learning models with PyTorch, Nikhil Ketkar, Jojo Moolayil</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
