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

By:

Michelucci, Umberto

Publication details: Apress 2024Edition: 2nd edDescription: xxviii, 380pISBN:

9781484283769

Subject(s):

DDC classification:

006.3/MIC

Summary: 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 openeddirectly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.

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Holdings
Item type	Current library	Call number	Status	Notes	Date due	Barcode
Books	Symbiosis Institute of Computer Studies and Research Data Mining	006.3/MIC (Browse shelf(Opens below))	Available	Intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.		SICSR-B-19678

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	005.741/KAN DATA MINING: CONCEPTS, MODELS, METHODS AND ALGORITHMS	006.3/HAL Managing AI in the Enterprise	006.3/KUL/SHI Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	006.3/MIC Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python	006.3/SAN Deep Reinforcement Learning with Python	006.3/VER Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras	006.312/BAR LEARNING TO LOVE DATA SCIENCE	Next

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 openeddirectly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.

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