دانلود کتاب شبکه های یادگیری عمیق

The reader is introduced to methods for designing, modeling, developing, building, training, and deploying deep learning in artificial intelligence applications. These include IoT, computer vision, natural language processing, and reinforcement learn-ing. A series of deep learning environment design and building exercises provide a succinct, project-driven deep learning tutorial. Furthermore, this book offers a comprehensive, consistent treatment of the current thinking and technology trends in this critical, rapidly expanding subject area. More importantly the book delivers fundamental and deep learning techniques and principles packed with real-world deep learning applications and examples. The book also provides a forward-thinking perspective in advanced deep learning infrastructure building and deployment methods. Implementation issues are discussed in a companion framework of deep learning networks that takes the reader through a logical sequence of discussions about core concepts and issues related to deep thinking.
This book is unique in that it offers a comprehensive, end-to-end look at deep learning principles and frameworks for building implementation algorithms. As with business organizations and government standards, implementation guidelines are structured and organized within an overall programming strategy. This structure is a valuable contribution of this book. This book is intended to serve as a valuable resource in the artificial intelligence discipline for students, professionals, and data scientists who want to understand how a successful implementation of deep learning algorithms and frameworks looks like from the programming context.
This book discusses feed-forward techniques and tools for setting up deep learning applications including CPU and GPU systems. The novelty of this book stems from the fact that it provides a comprehensive approach and toolset for developing deep learning applications and building virtual environments for various AI Platforms. It demonstrates and conveys core insights into various native AI hardware configurations including Edge Native AI hardware, and in setting up Jetson Nano in IoT Edge environment. It also describes and demonstrates the hand-holding techniques to configure the open-source operating system and edge devices. Advanced deep learning deployments are discussed and illustrated with specific examples that contribute to detailed understanding of real-time deep learning applications.
The book introduces the reader to neural nets, convolutional neural nets, word embeddings, recurrent or nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised learning models, and other basic concepts and methods. As important, this book covers artificial intelligence research on design, development, training, test, and deployment of applications and hardware services including the IoT micro services. TensorFlow, an open-source machine learning platform, allows students, AI experts, and data scientist professionals to work through programs and master the fundamentals of deep learning.
This book claims that building the best deep learning environment is the ultimate way to study deep learning science, and the book reflects this philosophy. Each section of the book presents stepwise command-oriented instructional guidelines to create, configure, and build the deep learning environments with the widely used Tensorflow and other relevant tools in various hardware and software environments.
Ultimately, this book aims to deliver deep learning insights with zero dependency or familiarity with probability, statistics, multivariate calculus, and linear algebra. It caters to a wide readership that includes both graduate and undergraduate students, practitioners, and researchers in academia.