Deep Learning in Practice (Paperback)

Mehdi Ghayoumi

ISBN 10: 0367456583 / ISBN 13: 9780367456580

Editore: Taylor & Francis Ltd, London, 2025

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Paperback. 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 networks 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. Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9780367456580

Dati bibliografici

• Titolo: Deep Learning in Practice (Paperback)
• Editore: Taylor & Francis Ltd, London
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 0367456583
• ISBN 13: 9780367456580
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Dr. Mehdi Ghayoumi is a course facilitator at Cornell University and adjunct faculty of Computer Science at the University of San Diego. Prior to this, he was a research assistant professor at SUNY at Binghamton, where he was the Media Core Lab’s dynamic leader. He was also a lecturer at Kent State University, where he received the Teaching Award for two consecutive years in 2016 and 2017. In addition, he has been teaching machine learning, data science, robotic and programming courses for several years.

Dr. Ghayoumi research interests are in Machine Learning, Machine Vision, Robotics, and Human-Robot Interaction (HRI). His research focuses are on building real systems for realistic environment settings, and his current projects have applications in Human-Robot Interaction, manufacturing, biometric, and healthcare.

He is a technical program committee member of several conferences, workshops, and editorial board member of several journals in machine learning, mathematics, and robotics, like ICML, ICPR, HRI, FG, WACV, IROS, CIBCB, and JAI. In addition, his research papers have been published at conferences and journals in the fields, including Human-Computer Interaction (HRI), Robotics Science and Systems (RSS), International Conference on Machine Learning and Applications (ICMLA), and others.