图书简介
Apply the technology of the future to networking and communications. Artificial intelligence, which enables computers or computer-controlled systems to perform tasks which ordinarily require human-like intelligence and decision-making, has revolutionized computing and digital industries like few other developments in recent history. Tools like artificial neural networks, large language models, and deep learning have quickly become integral aspects of modern life. With research and development into AI technologies proceeding at lightning speeds, the potential applications of these new technologies are all but limitless. AI Applications to Communications and Information Technologies offers a cutting-edge introduction to AI applications in one particular set of disciplines. Beginning with an overview of foundational concepts in AI, it then moves through numerous possible extensions of this technology into networking and telecommunications. The result is an essential introduction for researchers and for technology undergrad/grad student alike. AI Applications to Communications and Information Technologies readers will also find: In-depth analysis of both current and evolving applications Detailed discussion of topics including generative AI, chatbots, Automatic Speech Recognition, image classification and recognition, IoT, smart buildings, network management, network security, and more An authorial team with immense experience in both research and industry AI Applications to Communications and Information Technologies is ideal for researchers, industry observers, investors, and advanced students of network communications and related fields.
Ch 1: Overview 1.1 Introduction and Basic Concepts 1.1.1 Machine Learning 1.1.2 Deep Learning 1.1.3 Activation Functions 1.1.4 MLPs 1.1.5 RNNs 1.1.6 CNNs 1.1.7 Comparison 1.2 Learning Methods 1.3 Areas of applicability 1.4 Scope of this text References Glossary Ch 2: Current and evolving applications to Natural Language Processing 2.1 Scope 2.2 Introduction 2.3 Overview of Natural Language Processing and Speech Processing 2.3.1 Feed-forward NN 2.3.2 RNNs 2.3.3 LSTM 2.3.4 Attention 2.3.5 Transformer 2.4 NLP/NLU Basics 2.4.1 Pre-training 2.4.2 NLP/NLG Architectures 2.4.3 Encoder-Decoder Methods 2.4.4 Application of Transformer 2.4.5 Other Approaches 2.5 NLG Basics 2.6 Chatbots References Glossary Chapter 3: Current and evolving applications to Speech Processing 3.1 Scope 3.2 Overview 3.2.1 Traditional Approaches 3.2.2 DNN-based feature extraction 3.3 Noise Cancellation 3.3.1 Approaches 3.3.2 Example of a system supporting noise cancellation 3.4 Training 3.5 Applications to voice interfaces used to control home devices
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