 | Deep Learning in Practice Subjects: Computer Science; Engineering & Technology; Mathematics & Statistics; Artificial Intelligence; Computation; Computer Science (General); Systems & Control Engineering; Statistics & Probability; Cognitive Artificial Intelligence.; Human Computer Intelligence; Machine Learning - Design; Neural Networks; Robotics; Algorithms & Complexity; Machine Learning; Statistics; 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, NumPy, and TensorFlow fundamentals. E xplains and provides examples of deploying TensorFlow 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. 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. |
