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

Applied neural networks with TensorF...

Yalcin, Orhan Gazi.

Linked to FindBook

Google Book

Amazon

博客來

Applied neural networks with TensorFlow 2 = API oriented deep learning with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied neural networks with TensorFlow 2/ by Orhan Gazi Yalcin.
Reminder of title:	API oriented deep learning with Python /
Author:	Yalcin, Orhan Gazi.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xix, 295 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction -- Chapter 2: Introduction to Machine Learning -- Chapter 3: Deep Learning and Neutral Networks Overview -- Chapter 4: Complimentary Libraries to TensorFlow 2.x -- Chapter 5: A Guide to TensorFlow 2.0 and Deep Learning Pipeline -- Chapter 6: Feedfoward Neutral Networks -- Chapter 7: Convolutional Neural Networks -- Chapter 8: Recurrent Neural Networks -- Chapter 9: Natural Language Processing -- Chapter 10: Recommender Systems -- Chapter 11: Auto-Encoders -- Chapter 12: Generative Adversarial Networks.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-6513-0
ISBN:	9781484265130

Applied neural networks with TensorFlow 2 = API oriented deep learning with Python /
Yalcin, Orhan Gazi.

Applied neural networks with TensorFlow 2API oriented deep learning with Python /[electronic resource] :by Orhan Gazi Yalcin. - Berkeley, CA :Apress :2021. - xix, 295 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Introduction to Machine Learning -- Chapter 3: Deep Learning and Neutral Networks Overview -- Chapter 4: Complimentary Libraries to TensorFlow 2.x -- Chapter 5: A Guide to TensorFlow 2.0 and Deep Learning Pipeline -- Chapter 6: Feedfoward Neutral Networks -- Chapter 7: Convolutional Neural Networks -- Chapter 8: Recurrent Neural Networks -- Chapter 9: Natural Language Processing -- Chapter 10: Recommender Systems -- Chapter 11: Auto-Encoders -- Chapter 12: Generative Adversarial Networks.

Implement deep learning applications using TensorFlow while learning the "why" through in-depth conceptual explanations. You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy-others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. You will: Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks.

ISBN: 9781484265130

Standard No.: 10.1007/978-1-4842-6513-0doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .Y35 2021

Dewey Class. No.: 006.31

Applied neural networks with TensorFlow 2 = API oriented deep learning with Python /
LDR:03177nmm a2200325 a 4500 001 2236640
003 DE-He213
005 20201129080547.0
006 m d
007 cr nn 008maaau
008 211111s2021 cau s 0 eng d
020 $a 9781484265130 $q (electronic bk.)
020 $a 9781484265123 $q (paper)
024 7 $a 10.1007/978-1-4842-6513-0 $2 doi
035 $a 978-1-4842-6513-0
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a Q325.5 $b .Y35 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .Y16 2021
100 1 $a Yalcin, Orhan Gazi. $3 3488203
245 1 0 $a Applied neural networks with TensorFlow 2 $h [electronic resource] : $b API oriented deep learning with Python / $c by Orhan Gazi Yalcin.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xix, 295 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction -- Chapter 2: Introduction to Machine Learning -- Chapter 3: Deep Learning and Neutral Networks Overview -- Chapter 4: Complimentary Libraries to TensorFlow 2.x -- Chapter 5: A Guide to TensorFlow 2.0 and Deep Learning Pipeline -- Chapter 6: Feedfoward Neutral Networks -- Chapter 7: Convolutional Neural Networks -- Chapter 8: Recurrent Neural Networks -- Chapter 9: Natural Language Processing -- Chapter 10: Recommender Systems -- Chapter 11: Auto-Encoders -- Chapter 12: Generative Adversarial Networks.
520 $a Implement deep learning applications using TensorFlow while learning the "why" through in-depth conceptual explanations. You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy-others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. You will: Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks.
630 0 0 $a TensorFlow. $3 3446650
650 0 $a Machine learning. $3 533906
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Python (Computer program language) $3 729789
650 0 $a Artificial intelligence. $3 516317
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6513-0
950 $a Professional and Applied Computing (SpringerNature-12059)