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The book showcases the forefront of artificial intelligence with an in-depth examination of cutting-edge advancements, with technologies such as deep learning, neural networks, transfer learning, human-artificial intelligence collaboration, and sentiment analysis. * Explores the forefront of artificial intelligence with an in-depth examination of cutting-edge advancements, with technologies such as deep learning, neural networks, and transfer learning.
Discusses the realm of voice assistants, exploring how artificial intelligence enhances natural language processing and voice recognition capabilities and revolutionizes human-computer interaction paradigms. Provides insights into how deep learning, neural networks, machine learning, and data science intersect with broader ethical considerations, addressing issues such as transparency, and societal impact. Covers human-artificial intelligence collaboration and healthcare applications of artificial intelligence and data analytics. Explains generative adversarial networks and advanced techniques in recommender systems.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.
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The book showcases the forefront of artificial intelligence with an in-depth examination of cutting-edge advancements, with technologies such as deep learning, neural networks, transfer learning, human-artificial intelligence collaboration, and sentiment analysis. * Explores the forefront of artificial intelligence with an in-depth examination of cutting-edge advancements, with technologies such as deep learning, neural networks, and transfer learning.
Discusses the realm of voice assistants, exploring how artificial intelligence enhances natural language processing and voice recognition capabilities and revolutionizes human-computer interaction paradigms. Provides insights into how deep learning, neural networks, machine learning, and data science intersect with broader ethical considerations, addressing issues such as transparency, and societal impact. Covers human-artificial intelligence collaboration and healthcare applications of artificial intelligence and data analytics. Explains generative adversarial networks and advanced techniques in recommender systems.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.