Mehdi (University of San Diego) Ghayoumi 
Deep Learning in Practice 

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures.

Key features:

• D emonstrates a quick review on Python, Num Py, and Tensor Flow fundamentals.


• E xplains and provides examples of deploying Tensor Flow and Keras in several projects.


• E xplains the fundamentals of Artificial Neural Networks (ANNs).


• P resents several examples and applications of ANNs.


• L earning the most popular DL algorithms features.


• E xplains and provides examples for the DL algorithms that are presented in this book.


• A nalyzes the DL network’s parameter and hyperparameters.


• R eviews state-of-the-art DL examples.


• N ecessary and main steps for DL modeling.


• I mplements a Virtual Assistant Robot (VAR) using DL methods.


• N ecessary and fundamental information to choose a proper DL algorithm.


• G ives instructions to learn how to optimize your DL model IN PRACTICE .

This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.