東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep learning with Python = learn be...

Ketkar, Nikhil.

Linked to FindBook

Google Book

Amazon

博客來

Deep learning with Python = learn best practices of deep learning models with PyTorch /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning with Python/ by Nikhil Ketkar, Jojo Moolayil.
Reminder of title:	learn best practices of deep learning models with PyTorch /
Author:	Ketkar, Nikhil.
other author:	Moolayil, Jojo.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xvii, 306 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-5364-9
ISBN:	9781484253649

Deep learning with Python = learn best practices of deep learning models with PyTorch /
Ketkar, Nikhil.

Deep learning with Pythonlearn best practices of deep learning models with PyTorch /[electronic resource] :by Nikhil Ketkar, Jojo Moolayil. - Second edition. - Berkeley, CA :Apress :2021. - xvii, 306 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484253649

Standard No.: 10.1007/978-1-4842-5364-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: QA76.73.P98 / K485 2021

Dewey Class. No.: 005.133

Deep learning with Python = learn best practices of deep learning models with PyTorch /
LDR:03301nmm a2200337 a 4500 001 2240231
003 DE-He213
005 20210409022432.0
006 m d
007 cr nn 008maaau
008 211111s2021 cau s 0 eng d
020 $a 9781484253649 $q (electronic bk.)
020 $a 9781484253632 $q (paper)
024 7 $a 10.1007/978-1-4842-5364-9 $2 doi
035 $a 978-1-4842-5364-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98 $b K485 2021
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b K43 2021
100 1 $a Ketkar, Nikhil. $3 3236953
245 1 0 $a Deep learning with Python $h [electronic resource] : $b learn best practices of deep learning models with PyTorch / $c by Nikhil Ketkar, Jojo Moolayil.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xvii, 306 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Python (Computer program language) $3 729789
650 0 $a Data mining. $3 562972
650 1 4 $a Python. $3 3201289
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Open Source. $3 2210577
700 1 $a Moolayil, Jojo. $3 3383983
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-5364-9
950 $a Professional and Applied Computing (SpringerNature-12059)