語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Beginning Deep Learning with TensorFlow = Work with Keras, MNIST Data Sets, and Advanced Neural Networks /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Beginning Deep Learning with TensorFlow/ by Liangqu Long, Xiangming Zeng.
其他題名:
Work with Keras, MNIST Data Sets, and Advanced Neural Networks /
作者:
Long, Liangqu.
其他作者:
Zeng, Xiangming.
面頁冊數:
XXIII, 713 p. 323 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Machine Learning. -
電子資源:
https://doi.org/10.1007/978-1-4842-7915-1
ISBN:
9781484279151
Beginning Deep Learning with TensorFlow = Work with Keras, MNIST Data Sets, and Advanced Neural Networks /
Long, Liangqu.
Beginning Deep Learning with TensorFlow
Work with Keras, MNIST Data Sets, and Advanced Neural Networks /[electronic resource] :by Liangqu Long, Xiangming Zeng. - 1st ed. 2022. - XXIII, 713 p. 323 illus.online resource.
Chapter 1: Introduction to Artificial Intelligence -- Chapter 2. Regression -- Chapter 3. Classification -- Chapter 4. Basic Tensorflow -- Chapter 5. Advanced Tensorflow -- Chapter 6. Neural Network -- Chapter 7. Backward Propagation Algorithm -- Chapter 8. Keras Advanced API -- Chapter 9. Overfitting -- Chapter 10. Convolutional Neural Networks -- Chapter 11. Recurrent Neural Network -- Chapter 12. Autoencoder -- Chapter 13. Generative Adversarial Network (GAN) -- Chapter 14. Reinforcement Learning -- Chapter 15. Custom Dataset.
Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You’ll start with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications.
ISBN: 9781484279151
Standard No.: 10.1007/978-1-4842-7915-1doiSubjects--Topical Terms:
1137723
Machine Learning.
LC Class. No.: Q325.5-.7
Dewey Class. No.: 006.31
Beginning Deep Learning with TensorFlow = Work with Keras, MNIST Data Sets, and Advanced Neural Networks /
LDR
:03320nam a22003975i 4500
001
1093510
003
DE-He213
005
20220512153158.0
007
cr nn 008mamaa
008
221228s2022 xxu| s |||| 0|eng d
020
$a
9781484279151
$9
978-1-4842-7915-1
024
7
$a
10.1007/978-1-4842-7915-1
$2
doi
035
$a
978-1-4842-7915-1
050
4
$a
Q325.5-.7
072
7
$a
UYQM
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQM
$2
thema
082
0 4
$a
006.31
$2
23
100
1
$a
Long, Liangqu.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1401458
245
1 0
$a
Beginning Deep Learning with TensorFlow
$h
[electronic resource] :
$b
Work with Keras, MNIST Data Sets, and Advanced Neural Networks /
$c
by Liangqu Long, Xiangming Zeng.
250
$a
1st ed. 2022.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2022.
300
$a
XXIII, 713 p. 323 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
Chapter 1: Introduction to Artificial Intelligence -- Chapter 2. Regression -- Chapter 3. Classification -- Chapter 4. Basic Tensorflow -- Chapter 5. Advanced Tensorflow -- Chapter 6. Neural Network -- Chapter 7. Backward Propagation Algorithm -- Chapter 8. Keras Advanced API -- Chapter 9. Overfitting -- Chapter 10. Convolutional Neural Networks -- Chapter 11. Recurrent Neural Network -- Chapter 12. Autoencoder -- Chapter 13. Generative Adversarial Network (GAN) -- Chapter 14. Reinforcement Learning -- Chapter 15. Custom Dataset.
520
$a
Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You’ll start with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications.
650
1 4
$a
Machine Learning.
$3
1137723
650
0
$a
Machine learning.
$3
561253
700
1
$a
Zeng, Xiangming.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1401459
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484279144
776
0 8
$i
Printed edition:
$z
9781484279168
776
0 8
$i
Printed edition:
$z
9781484284049
856
4 0
$u
https://doi.org/10.1007/978-1-4842-7915-1
912
$a
ZDB-2-CWD
912
$a
ZDB-2-SXPC
950
$a
Professional and Applied Computing (SpringerNature-12059)
950
$a
Professional and Applied Computing (R0) (SpringerNature-43716)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入