{"product_id":"9781394427918","title":"Deep Learning in the Visual Domain Backpropagation in Theory, Code and Practice for Convolutional Networks and Visual Transformers","description":"\u003ch1\u003eDeep Learning in the Visual Domain\u003c\/h1\u003e\u003ch2\u003eBackpropagation in Theory, Code and Practice for Convolutional Networks and Visual Transformers\u003c\/h2\u003e\u003ch3\u003eJosé Solomon | Francois Charette\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Data Science \/ Neural Networks\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eBackpropagation from mathematical  first principles to Python implementation and autonomous driving\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eMost deep learning texts rely leave practitioners dependent on framework abstractions that hide what's happening or equipped with theory but no working code. \u003ci\u003eDeep Learning in the Visual Domain: Backpropagation in Theory, Code and Practice for Convolutional Networks and Visual Transformers\u003c\/i\u003e, written by two experienced AI researchers, closes that gap by deriving the s of each layer and implementing it in Python from scratch – so readers see exactly how networks learn. \u003c\/p\u003e\n\u003cp\u003eCoverage progresses from image filter fundamentals and neural network building blocks through convolutional networks and visual transformers, culminating in the design and implementation of autonomous driving models that navigate vehicles through a synthetic cityscape. Dedicated chapters derive the forward and backward passes for each layer type and pair them with corresponding Python code, making backpropagation itself – not just its effects – visible at every step. \u003c\/p\u003e\n\u003cp\u003eReaders will also find: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003ePython code implementations of the CNN and ViT architectures, providing full transparency into weight updates and gradient flow without framework abstraction\u003c\/li\u003e \u003cli\u003eStep-by-step backpropagation derivations for foundational layers, including the attention mechanism\u003c\/li\u003e \u003cli\u003eThe e2e_driver simulation environment for designing, training, and evaluating end-to-end autonomous driving models in a virtual cityscape\u003c\/li\u003e \u003cli\u003eAn active online community and supplementary content maintained by the authors to support ongoing learning and development\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eDeep learning professionals, graduate students, and senior undergraduates studying neural networks will find this book uniquely suited to building architectures from first principles. By uniting mathematical exposition with transparent code and a working autonomous driving application, it delivers the depth required to design networks from the ground up.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003cb\u003eJosé Solomon, PhD, \u003c\/b\u003eis the lead architect of the deep learning framework for an analog AI accelerator chip at Sagence AI. He holds two patents for deep learning algorithms in the autonomous vehicle space, co-founded a medical imaging start-up, and previously deployed models on Graphcore's IPU. He is a Sloan Foundation and National Science Foundation fellowship recipient. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eFrançois Charette, PhD, \u003c\/b\u003eis a retired AI\/ML Senior Research Scientist formerly with the DeepDSP Group at Palo Alto Greenfield Labs and Ford Motor Company. His career spans applied deep learning research in industrial and automotive domains, contributing directly to production-level neural network architectures and optimization strategies. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e02 February 2027\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-IEEE Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781394427918\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e208\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":46536412463244,"sku":"9781394427918","price":117.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781394427918.jpg?v=1781091688","url":"https:\/\/lateknightbooks.com\/products\/9781394427918","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}