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220922 ||| eng |
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|a 9781484280201
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4 |
|a QA76.73.P98
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| 100 |
1 |
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|a Michelucci, Umberto
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| 245 |
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|a Applied deep learning with TensorFlow 2
|b learn to implement advanced deep learning techniques with Python
|c Umberto Michelucci
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| 250 |
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|a 2nd ed
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| 260 |
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|a New York, NY
|b Apress
|c 2022
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| 300 |
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|a xxviii, 380 pages : illustrations
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| 505 |
0 |
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|a Includes bibliographical references and index
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Neural networks (Computer science) / http://id.loc.gov/authorities/subjects/sh90001937
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Réseaux neuronaux (Informatique)
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| 653 |
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|a Neural networks (Computer science) / fast
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Neural Networks, Computer
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| 653 |
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|a Machine Learning
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
0 |
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|a ITpro collection
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| 028 |
5 |
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|a 10.1007/978-1-4842-8020-1
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| 776 |
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|z 1484280202
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| 776 |
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|z 9781484280195
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| 776 |
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|z 1484280199
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| 776 |
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|z 9781484280201
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781484280201/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 331
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|a 500
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|a 006.31
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| 520 |
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|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
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