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Beginning deep learning with TensorF...

Long, Liangqu.

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Beginning deep learning with TensorFlow = work with Keras, MNIST data sets, and advanced neural networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginning deep learning with TensorFlow/ by Liangqu Long, Xiangming Zeng.
Reminder of title:	work with Keras, MNIST data sets, and advanced neural networks /
Author:	Long, Liangqu.
other author:	Zeng, Xiangming.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xxiii, 713 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	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 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

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

TensorFlow.
Subjects--Topical Terms:

533906
Machine learning.

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

Dewey Class. No.: 006.31

Beginning deep learning with TensorFlow = work with Keras, MNIST data sets, and advanced neural networks /
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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.
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