國立高雄大學圖資館 |

登入

回首頁 到查詢結果 [ subject:"Machine Learning." ]

Beginning deep learning with TensorFlowwork with Keras, MNIST data sets, and advanced neural networks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Beginning deep learning with TensorFlowby 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.
出版者:	Berkeley, CA :Apress :2022.
面頁冊數:	xxiii, 713 p. :ill., digital ;24 cm.
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$q(electronic bk.)

Beginning deep learning with TensorFlowwork with Keras, MNIST data sets, and advanced neural networks /
Long, Liangqu.

Beginning deep learning with TensorFlowwork with Keras, MNIST data sets, and advanced neural networks /[electronic resource] :by Liangqu Long, Xiangming Zeng. - Berkeley, CA :Apress :2022. - xxiii, 713 p. :ill., digital ;24 cm.

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$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-7915-1doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .L65 2022

Dewey Class. No.: 006.31

Beginning deep learning with TensorFlowwork with Keras, MNIST data sets, and advanced neural networks /
LDR:02969nmm a2200325 a 4500 001 619935
003 DE-He213
005 20220127133232.0
006 m d
007 cr nn 008maaau
008 221103s2022 cau s 0 eng d
020 $a 9781484279151$q(electronic bk.)
020 $a 9781484279144$q(paper)
024 7 $a 10.1007/978-1-4842-7915-1 $2 doi
035 $a 978-1-4842-7915-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .L65 2022
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
090 $a Q325.5 $b .L848 2022
100 1 $a Long, Liangqu. $3 920481
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.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xxiii, 713 p. : $b ill., digital ; $c 24 cm.
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.
630 0 0 $a TensorFlow. $3 864055
650 0 $a Machine learning. $3 188639
650 1 4 $a Machine Learning. $3 833608
700 1 $a Zeng, Xiangming. $3 920482
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-7915-1
950 $a Professional and Applied Computing (SpringerNature-12059)