國立虎尾科技大學 |

Applied Deep Learning with TensorFlow 2 = Learn to Implement Advanced Deep Learning Techniques with Python /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Applied Deep Learning with TensorFlow 2/ by Umberto Michelucci.
其他題名:	Learn to Implement Advanced Deep Learning Techniques with Python /
作者:	Michelucci, Umberto.
面頁冊數:	XXVIII, 380 p. 148 illus., 31 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Python. -
電子資源:	https://doi.org/10.1007/978-1-4842-8020-1
ISBN:	9781484280201

Applied Deep Learning with TensorFlow 2 = Learn to Implement Advanced Deep Learning Techniques with Python /
Michelucci, Umberto.

Applied Deep Learning with TensorFlow 2Learn to Implement Advanced Deep Learning Techniques with Python /[electronic resource] :by Umberto Michelucci. - 2nd ed. 2022. - XXVIII, 380 p. 148 illus., 31 illus. in color.online resource.

Chapter 1 : Optimization and Neural Networks -- Chapter 2: Hands-on with One Single Neuron -- Chapter 3: Feed Forward Neural Networks.-Chapter 4: Regularization -- Chapter 5: Advanced Optimizers -- Chapter 6: Hyperparameter Tuning -- Chapter 7: Convolutional Neural Networks.-Chapter 8: Brief Introduction to Recurrent Neural Networks -- Chapter 9: Autoencoders -- Chapter 10: Metric Analysis -- Chapter 11: General Adversarial Networks (GANs) -- Appendix A: Introduction to Keras -- Appendix B: Customizing Keras.

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.

ISBN: 9781484280201

Standard No.: 10.1007/978-1-4842-8020-1doiSubjects--Topical Terms:

1115944
Python.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Applied Deep Learning with TensorFlow 2 = Learn to Implement Advanced Deep Learning Techniques with Python /
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