TY  - GEN
N2  - 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.
DO  - 10.1007/978-1-4842-8020-1
DO  - doi
AB  - 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.
T1  - Applied deep learning with TensorFlow 2 :learn to implement advanced deep learning techniques with Python /
AU  - Michelucci, Umberto,
ET  - 2nd ed.
CN  - QA76.73.P98
ID  - 1445676
KW  - Python (Computer program language)
KW  - Machine learning.
KW  - Neural networks (Computer science)
KW  - Python (Langage de programmation)
KW  - Apprentissage automatique.
KW  - Réseaux neuronaux (Informatique)
SN  - 9781484280201
SN  - 1484280202
TI  - Applied deep learning with TensorFlow 2 :learn to implement advanced deep learning techniques with Python /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8020-1
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8020-1
ER  -