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 tha...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
[2022]
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| Ausgabe: | Second edition |
| Schlagworte: | |
| Zusammenfassung: | 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.You will: • Understand the fundamental concepts of how neural networks work• Learn the fundamental ideas behind autoencoders and generative adversarial networks• Be able to try all the examples with complete code examples that you can expand for your own projects• Have available a complete online companion book with examples and tutorials.This book is for:Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming |
| Beschreibung: | xxviii, 380 Seiten Illustrationen, Diagramme 773 grams |
| ISBN: | 9781484280195 |
Internformat
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Datensatz im Suchindex
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| author | Michelucci, Umberto |
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| id | DE-604.BV048203250 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:47:13Z |
| indexdate | 2024-07-10T09:31:54Z |
| institution | BVB |
| isbn | 9781484280195 |
| language | English |
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| oclc_num | 1334038114 |
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| physical | xxviii, 380 Seiten Illustrationen, Diagramme 773 grams |
| publishDate | 2022 |
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| publisher | Apress |
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| spelling | Michelucci, Umberto Verfasser (DE-588)1170709281 aut Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python Umberto Michelucci Second edition Berkeley, CA Apress [2022] © 2022 xxviii, 380 Seiten Illustrationen, Diagramme 773 grams txt rdacontent n rdamedia nc rdacarrier 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.You will: • Understand the fundamental concepts of how neural networks work• Learn the fundamental ideas behind autoencoders and generative adversarial networks• Be able to try all the examples with complete code examples that you can expand for your own projects• Have available a complete online companion book with examples and tutorials.This book is for:Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming bicssc bisacsh Machine learning Python (Computer program language) Artificial intelligence Hardcover, Softcover / Informatik, EDV/Informatik Erscheint auch als Online-Ausgabe 978-1-4842-8020-1 |
| spellingShingle | Michelucci, Umberto Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python bicssc bisacsh Machine learning Python (Computer program language) Artificial intelligence |
| title | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python |
| title_auth | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python |
| title_exact_search | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python |
| title_exact_search_txtP | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python |
| title_full | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python Umberto Michelucci |
| title_fullStr | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python Umberto Michelucci |
| title_full_unstemmed | Applied deep learning with TensorFlow 2 learn to implement advanced deep learning techniques with Python Umberto Michelucci |
| title_short | Applied deep learning with TensorFlow 2 |
| title_sort | applied deep learning with tensorflow 2 learn to implement advanced deep learning techniques with python |
| title_sub | learn to implement advanced deep learning techniques with Python |
| topic | bicssc bisacsh Machine learning Python (Computer program language) Artificial intelligence |
| topic_facet | bicssc bisacsh Machine learning Python (Computer program language) Artificial intelligence |
| work_keys_str_mv | AT michelucciumberto applieddeeplearningwithtensorflow2learntoimplementadvanceddeeplearningtechniqueswithpython |