Applied deep learning with TensorFlow 2 : learn to implement advanced deep learning techniques with Python / Umberto Michelucci.
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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| Online Access: |
Full Text (via Skillsoft) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Apress,
[2022]
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| Edition: | 2nd ed. |
| Series: | ITpro collection
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| Subjects: |
MARC
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| 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. |
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