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Deep learning projects using TensorF...

Silaparasetty, Vinita.

Deep learning projects using TensorFlow 2neural network development with Python and Keras /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning projects using TensorFlow 2by Vinita Silaparasetty.
Reminder of title:	neural network development with Python and Keras /
Author:	Silaparasetty, Vinita.
Published:	Berkeley, CA :Apress :2020.
Description:	xxv, 421 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-5802-6
ISBN:	9781484258026$q(electronic bk.)

Deep learning projects using TensorFlow 2neural network development with Python and Keras /
Silaparasetty, Vinita.

Deep learning projects using TensorFlow 2neural network development with Python and Keras /[electronic resource] :by Vinita Silaparasetty. - Berkeley, CA :Apress :2020. - xxv, 421 p. :ill., digital ;24 cm.

Chapter 1: Getting Started: Installation and Troubleshooting -- Chapter 2: Perceptrons -- Chapter 3: Neural Networks -- Chapter 4: Sentiment Analysist -- Chapter 5: Music Generation -- Chapter 6: Image Colorization -- Chapter 7: Image Deblurring -- Chapter 8: Image Manipulation -- Chapter 9: Neutral Network Collection -- Appendix: Portfolio Tips.

Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. You will: Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes.

ISBN: 9781484258026$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-5802-6doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Deep learning projects using TensorFlow 2neural network development with Python and Keras /
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505 0 $a Chapter 1: Getting Started: Installation and Troubleshooting -- Chapter 2: Perceptrons -- Chapter 3: Neural Networks -- Chapter 4: Sentiment Analysist -- Chapter 5: Music Generation -- Chapter 6: Image Colorization -- Chapter 7: Image Deblurring -- Chapter 8: Image Manipulation -- Chapter 9: Neutral Network Collection -- Appendix: Portfolio Tips.
520 $a Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. You will: Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes.
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