{"id":7367,"date":"2025-09-08T12:29:47","date_gmt":"2025-09-08T12:29:47","guid":{"rendered":"https:\/\/jecpublication.com\/?post_type=product&#038;p=7367"},"modified":"2025-09-10T11:01:32","modified_gmt":"2025-09-10T11:01:32","slug":"advanced-deep-learning-architectures-beyond-cnns-and-lstms","status":"publish","type":"product","link":"https:\/\/jecpublication.com\/index.php\/product\/advanced-deep-learning-architectures-beyond-cnns-and-lstms\/","title":{"rendered":"ADVANCED DEEP LEARNING ARCHITECTURES Beyond CNNs and LSTMs"},"content":{"rendered":"<p><strong><strong>ISBN:<\/strong><\/strong>978-93-6976-068-8<\/p>\n<p><strong><strong>Authors:<\/strong><\/strong><br \/>\nMrs. Shubhashree Sahoo<br \/>\nDr. S Rao Chintalapudi<br \/>\nDr. Sayanti Chatterjee<br \/>\nMr. Kancharagunta Kishan Babu<br \/>\nMr. Bhupesh Deka<\/p>\n<p><strong><strong>Total Pages:<\/strong><\/strong>292<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep learning has fundamentally transformed the landscape of artificial intelligence, powering<br \/>\nbreakthroughs in computer vision, natural language processing, speech recognition, and beyond.<br \/>\nOver the past decade, Convolutional Neural Networks (CNNs) and Long Short-Term Memory<br \/>\nnetworks (LSTMs) emerged as the dominant architectures for tackling structured image data and<br \/>\nsequential information, respectively. CNNs, with their hierarchical feature extraction and local<br \/>\nreceptive fields, revolutionized image classification, object detection, and semantic segmentation,<br \/>\nenabling systems to achieve near-human or even superhuman performance on benchmarks such<br \/>\nas ImageNet. LSTMs, on the other hand, overcame the limitations of traditional recurrent neural<br \/>\nnetworks, effectively addressing the vanishing gradient problem and enabling long-range<br \/>\ntemporal dependencies to be learned, thereby advancing language modeling, machine translation,<br \/>\nand speech synthesis.<br \/>\nHowever, as datasets expanded to billions of samples, tasks grew increasingly complex, and the<br \/>\nneed for generalization across heterogeneous domains intensified, the inherent limitations of<br \/>\nCNNs and LSTMs became apparent. CNNs struggle with modeling long-range dependencies,<br \/>\nprocessing irregular graph-structured data, or integrating multimodal inputs, while LSTMs face<br \/>\nchallenges in parallelization, handling very long sequences efficiently, and scaling to massive<br \/>\nmodel sizes. Moreover, simply increasing depth, width, or training data does not always guarantee<br \/>\nproportional improvements in performance, highlighting the plateauing effect of these<br \/>\narchitectures in cutting-edge AI applications.<br \/>\nThis book, Advanced Deep Learning Architectures: Beyond CNNs and LSTMs, is designed to chart<br \/>\nthe evolution of next-generation architectures that address these limitations and define the future<br \/>\nof artificial intelligence. It offers a comprehensive journey through the design principles,<br \/>\nmathematical foundations, and practical implementations of state-of-the-art models. The scope<br \/>\nspans transformers \u2014 including BERT, GPT, and Vision Transformers \u2014 which leverage selfattention to model long-range dependencies and multimodal relationships; graph neural networks,<br \/>\nwhich enable AI systems to reason about relational and non-Euclidean data; capsule networks,<br \/>\nwhich improve spatial hierarchies and pose awareness; neural ordinary differential equations,<br \/>\nwhich introduce continuous-time modeling; and diffusion models, which have redefined<br \/>\ngenerative AI through probabilistic modeling and denoising frameworks. The book also delves<br \/>\ninto hybrid and multimodal architectures, advanced reinforcement learning frameworks, and<br \/>\nemerging paradigms such as neuromorphic computing, quantum deep learning, and evolutionary algorithms. Importantly, the text bridges theory and practice. Each chapter combines rigorous<br \/>\nmathematical derivations \u2014 from linear algebra and tensor calculus to probabilistic modeling and<br \/>\ninformation-theoretic analysis \u2014 with hands-on Python implementations using libraries such as<br \/>\nPyTorch, TensorFlow, and NumPy. Readers are guided through step-by-step coding exercises,<br \/>\npractical tips for optimization, and demonstrations of applying these models to real-world datasets.<br \/>\nThis dual approach ensures a deep understanding not just of why these architectures work, but<br \/>\nhow to implement, experiment, and extend them.<br \/>\nBeyond technical mastery, the book emphasizes the broader implications of advanced AI:<br \/>\nmultimodal intelligence, ethical alignment, interpretability, and the path toward general-purpose<br \/>\nAI systems. <\/p>\n","protected":false},"featured_media":7368,"comment_status":"open","ping_status":"closed","template":"","meta":[],"authors":[],"publishers":[],"product_cat":[207,45],"product_tag":[],"class_list":{"0":"post-7367","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","5":"hentry","6":"product_cat-academic","7":"product_cat-english","8":"entry","9":"has-media","11":"owp-thumbs-layout-horizontal","12":"owp-btn-normal","13":"owp-tabs-layout-horizontal","14":"has-no-thumbnails"},"_links":{"self":[{"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/product\/7367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/comments?post=7367"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/media\/7368"}],"wp:attachment":[{"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/media?parent=7367"}],"wp:term":[{"taxonomy":"authors","embeddable":true,"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/authors?post=7367"},{"taxonomy":"publishers","embeddable":true,"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/publishers?post=7367"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/product_cat?post=7367"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/jecpublication.com\/index.php\/wp-json\/wp\/v2\/product_tag?post=7367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}