Overview

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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