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13.0 MB
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3AE4B3ACF7BFB27D07C8F851FB852F78FD0D3E9B
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June 19, 2025, 1:31 p.m.
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(Last updated: June 19, 2025, 1:32 p.m.)
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| ['Ramamurthy K. Generative AI Unleashed. Advancements, transformative apps...2025.pdf'] | 0 bytes |
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13.0 MB
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2025-06-19
| Uploaded by andryold1 | Size 13.0 MB | Health [ 27 /24 ] | Added 2025-06-19 |
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13.4 MB
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2025-11-21
| Uploaded by andryold1 | Size 13.4 MB | Health [ 85 /30 ] | Added 2025-11-21 |
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SOURCE: Ramamurthy K. Generative AI Unleashed. Advancements, transformative apps...2025
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COVER
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MEDIAINFO
Textbook in PDF format Today's Generative AI has been marked by the advent of neural networks, inspired by the human brain, which are trained to recognize patterns in a dataset. Once the network is trained, it can make decisions or predictions without being programmed to perform tasks. Generative AI learns from a set of data without explicit instructions and can create and generate new digital content such as text, audio and art. Recent models are beginning to overcome challenges such as computational power, data quality and training stability. This edited book on Generative AI presents novel perspectives, approaches and methodologies, as well as security, ethical and legal considerations, and future trends. Topics and technologies covered include generative transformers and text generation models, generative models for human-like speech synthesis, Generative AI for image synthesis, data synthesis for privacy protection, and exploration of the impact of Generative AI in fields including industry 4.0, astronomy, and brain tumour detection. Two chapters offer perspectives on ethics and legality. Because of their exceptional NLP capabilities, LLMs have gained considerable interest from academia and business in recent years, making them a key focus in the AI area. LLMs are advanced deep learning frameworks initially pretrained on extensive textual datasets and subsequently fine-tuned to align closely with human preferences. The advent of the generative transformer is the conceptual breakthrough that underpins LLMs. A novel model architecture called the generative transformer completely relies on attention techniques to control global relationships between input and output vectors, doing away with recurrence. This method employs self-attention, generating representations of input and output sequences independently of sequence-aligned RNNs or convolutional layers, transformed transduction models, including language translation models. Transformers are characterized by their encoder–decoder structure a hallmark of transduction models. Generative AI Unleashed: Advancements, transformative applications and future frontiers will serve as a valuable resource for researchers, engineers, advanced students and lecturers operating in the domains that are significantly impacted by Generative AI
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