Generative AI: Current Trends and Applications
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Beschreibung
This book focuses on the latest advancements in generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as generative AI models, transfer learning, and natural language processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK, and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of generative AI and how it can be harnessed to tackle complex real-world challenges.
This comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as Generative AI models, transfer learning and Natural Language Processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in Generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of Generative AI and how it can be harnessed to tackle complex real-world challenges.
Presents basic concepts of Generative AI Includes state-of-the-art applications Illustrates different case studies related to NLP, LLMs, cryptography, text and image generation
Autor*in
Khalid Raza
Themen in »Generative AI: Current Trends and Applications«
Generative Adversarial Networks (GANs) AI Image Generation AI Text Generation Transfer Learning Few-Shot Learning Transformer Encoder and Decoder Self Attention Networks