000			nam a22 4500
999			_c32777 _d32777
008			240216b xxu||||| |||| 00| 0 eng d
020			_a9780367458621
082			_a006.31 _bGHA
100			_aGhayoumi, Mehdi
245			_aDeep learning in practice
260			_bCRC Press, _aBoca Raton : _c2022
300			_axix, 198 p. ; _c25 cm. _bill., (b & w),
365			_b69.99 _c₤ _d94.80
504			_aIncludes bibliographical references and index.
520			_aDeep 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 network⁰́₉s 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.
650			_aDeep learning
650			_aBusiness statistics
650			_aSconomics statistics
650			_aComputer science
650			_aMachine learning
650			_aNeural networks
650			_aReshape array
650			_aMatrix multiplication
650			_aPython libraries
650			_aObject recognition
942			_2ddc _cBK