東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep learning with Python = a hands-...

Ketkar, Nikhil.

FindBook

Google Book

Amazon

博客來

Deep learning with Python = a hands-on introduction /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning with Python/ by Nikhil Ketkar.
其他題名:	a hands-on introduction /
作者:	Ketkar, Nikhil.
出版者:	Berkeley, CA :Apress : : 2017.,
面頁冊數:	xv, 160 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	http://dx.doi.org/10.1007/978-1-4842-2766-4
ISBN:	9781484227664

Deep learning with Python = a hands-on introduction /
Ketkar, Nikhil.

Deep learning with Pythona hands-on introduction /[electronic resource] :by Nikhil Ketkar. - Berkeley, CA :Apress :2017. - xv, 160 p. :ill., digital ;24 cm.

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.

ISBN: 9781484227664

Standard No.: 10.1007/978-1-4842-2766-4doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Deep learning with Python = a hands-on introduction /
LDR:02355nmm a2200313 a 4500 001 2097813
003 DE-He213
005 20170418141517.0
006 m d
007 cr nn 008maaau
008 171229s2017 cau s 0 eng d
020 $a 9781484227664 $q (electronic bk.)
020 $a 9781484227657 $q (paper)
024 7 $a 10.1007/978-1-4842-2766-4 $2 doi
035 $a 978-1-4842-2766-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98
072 7 $a UMA $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a COM018000 $2 bisacsh
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b K43 2017
100 1 $a Ketkar, Nikhil. $3 3236953
245 1 0 $a Deep learning with Python $h [electronic resource] : $b a hands-on introduction / $c by Nikhil Ketkar.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2017.
300 $a xv, 160 p. : $b ill., digital ; $c 24 cm.
520 $a Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.
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 Computer Science. $3 626642
650 2 4 $a Computing Methodologies. $3 830243
650 2 4 $a Programming Techniques. $3 892496
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Mathematical Logic and Formal Languages. $3 892517
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-2766-4
950 $a Professional and Applied Computing (Springer-12059)