Deep Learning: Theoretical and Practical Approach
Sina Ranjbar Kooh Farhadi · Zenodo (CERN European Organization for Nuclear Research) · 2022
Deep Learning Course Book In Persian This book includes three parts; The first part provides necessary prerequisites for deep learning topics such as linear algebra, statistics and probability, information theory, data mining, signal processing, machine learning, etc. The main issues in deep learning, including artificial neural networks, evaluation criteria, optimization methods, represent learning, recurrent neural networks, convolutional neural networks, and generative networks, fall within the scope of the second part. Also, the third part of this book is dedicated to advanced topics in this field. Natural language models, attention mechanism, transfer learning, domain adaption, and neural architecture search are examples of the titles of this part.