Applied deep learning with TensorFlow 2 : learn to implement advanced deep learning techniques with Python

Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so th...

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Bibliographic Details
Main Author: Michelucci, Umberto, (Author)
Format: eBook
Language: English
Published: New York, NY : Apress, [2022]
Edition: 2nd ed.
Series: ITpro collection
Subjects:
Python (Computer program language)
Machine learning.
Neural networks (Computer science)
elektronické knihy
electronic books
ISBN: 9781484280201
1484280202
9781484280195
1484280199
Physical Description: 1 online resource (xxviii, 380 pages : illustrations)

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Table of contents

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020 |a 9781484280201  |q (electronic bk.) 
020 |a 1484280202  |q (electronic bk.) 
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024 7 |a 10.1007/978-1-4842-8020-1  |2 doi 
035 |a (OCoLC)1308983931  |z (OCoLC)1306528775  |z (OCoLC)1308394592  |z (OCoLC)1308796002  |z (OCoLC)1312916752  |z (OCoLC)1324244058  |z (OCoLC)1341685672 
100 1 |a Michelucci, Umberto,  |e author. 
245 1 0 |a Applied deep learning with TensorFlow 2 :  |b learn to implement advanced deep learning techniques with Python /  |c Umberto Michelucci. 
250 |a 2nd ed. 
264 1 |a New York, NY :  |b Apress,  |c [2022] 
264 4 |c ©2022 
300 |a 1 online resource (xxviii, 380 pages : illustrations) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 0 |a ITpro collection 
504 |a Includes bibliographical references and index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. 
505 0 0 |t Optimization and Neural Networks --  |t Hands-on with a single neuron --  |t Feed-Forward Neural Networks --  |t Regularization --  |t Advanced optimizers --  |t Hyper-parameter tuning --  |t Convolutional neural networks --  |t A Brief Introduction to Recurrent Neural Networks --  |t Autoencoders --  |t Metric analysis --  |t Generative Adversarial Networks (GANs) --  |t Appendix A: Introduction to Keras --  |t Appendix B: customizing Keras --  |t Index. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Python (Computer program language) 
650 0 |a Machine learning. 
650 0 |a Neural networks (Computer science) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |c Original  |z 1484280199  |z 9781484280195  |w (OCoLC)1289363782 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpADLTFLI1/applied-deep-learning?kpromoter=marc  |y Full text 

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