Paperback. This comprehensive guide explores cutting-edge deep learning models and their real-world applications. Covering convolutional, recurrent, and attention-based networks, it dives deep into the architectures that power modern AI. From CNNs and RNNs to transformers, LLMs, and GNNs, the book offers both theoretical insights and practical case studies. It also addresses efficiency, scalability, and ethical considerations in deep learning. What's Inside? CNNs for detection, segmentation, and video RNNs, LSTMs, GRUs, and sequence modeling Transformers, attention, and vision transformers LLM training, fine-tuning, and evaluation GNNs for classification, link prediction, and recommendation GANs, autoencoders, and generative models Deployment, pruning, quantization, and robustness Why This Book?Because mastering modern deep learning architectures is essential for anyone building advanced AI systems today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Advanced Deep Learning Architectures (Paperback)
Publisher: Independently Published
Publication Date: 2025
Binding: Paperback
Condition: new

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