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 ...
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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.
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Add this copy of Advanced Deep Learning Architectures: CNNs, RNNs, to cart. $19.31, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
