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:

• Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals.
• Explains and provides examples of deploying TensorFlow and Keras in several projects.
• Explains the fundamentals of Artificial Neural Networks (ANNs).
• Presents several examples and applications of ANNs.
• Learning the most popular DL algorithms features.
• Explains and provides examples for the DL algorithms that are presented in this book.
• Analyzes the DL network’s parameter and hyperparameters.
• Reviews state-of-the-art DL examples.
• Necessary and main steps for DL modeling.
• Implements a Virtual Assistant Robot (VAR) using DL methods.
• Necessary and fundamental information to choose a proper DL algorithm.
• Gives 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.

Mehdi Ghayoumi is a research assistant professor of computer science at State University of New York at Binghamton and he is also the dynamic Leader of Media Core Lab . Ghayoumi’s research interests are in the areas of Machine Learning, Robotic, Human Computer Interaction, and Machine Vision. His current research projects have applications in Human Robot Interaction, biometric and healthcare. Mehdi received a Ph.D. in Computer Science from the Kent State University and Research fellowship award. Ghayoumi served as Lecturer (part time faculty member) at the KSU from fall 2014 To 2017 and he has received the Kent State University Teaching Award twice (2016-17).