Encoder-Decoder Architectures Explained
Master the Fundamentals of Sequence-to-Sequence Learning with Convolutional Neural Networks
Encoder-decoder architectures are everywhere in modern AI—from language translation to image captioning—yet many practitioners struggle to understand how these systems actually work beneath the surface. You might know that encoders compress information and decoders generate output, but do you truly grasp the mechanics of how information flows through these networks, or how to implement them effectively for your specific problems? This book bridges that gap. Starting with the fundamental concepts of neural network building blocks, you'll progress through the complete architecture of encoder-decoder systems, explore how convolutional networks enhance these models, and discover the attention mechanisms that make them powerful. Each concept is explained with clear examples, practical code insights, and real-world applications. Whether you're building machine translation systems, working with image-to-text models, or exploring sequence-to-sequence learning, this guide equips you with both the theoretical understanding and practical knowledge needed to implement, optimize, and innovate with encoder-decoder architectures.
Est. Length
222 pages (10 chapters)
Language
English
Publication date
Nov 10, 2025
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