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Overview: This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. In recent decades, machines have played a significant role in making human life more comfortable. Machine Learning (ML) is currently one of the most advanced and popular subjects, with numerous applications across various fields. Making machines smarter to better assist humans is a challenge that many enjoy solving, and researchers are working toward this goal, continuously seeking ways to enhance machine intelligence. Numerous algorithms and techniques have been discovered and developed in this field, some traditional, such as Support Vector Machine (SVM) or Decision Tree (DT), and some novel, such as Deep Learning. These methods and studies form part of Machine Learning, an area of Computer Science that has recently piqued both academic and industry interests. Generative Adversarial Networks (GANs) are one of the most exciting and recently developed Deep Learning methods, showing several promising results in generating new data from existing data. They have a wide range of applications, which we will discuss in this book, along with information about their architectures and methods, as well as step-by-step instructions for implementing them.
Genre: Non-Fiction > Tech & Devices
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