{"product_id":"math-architectures-of-deep-learning","title":"Math \u0026 Architectures Of Deep Learning","description":"\u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e   The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function.    \u003cb\u003e\u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003eMath and Architectures of Deep Learning\u003c\/i\u003e \u003c\/b\u003ebridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you''ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.  \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the technology\u003c\/h2\u003e  \u003ca name=\"about-the-technology\" class=\"anchor\" style=\"box-sizing: border-box; background-color: transparent; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003c\/a\u003eIt''s important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You''ll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you''ll be glad you can quickly identify and fix problems. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the book\u003c\/h2\u003e  \u003ca name=\"about-the-book\" class=\"anchor\" style=\"box-sizing: border-box; background-color: transparent; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative;\"\u003e\u003c\/a\u003e  \u003cb\u003e\u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003eMath and Architectures of Deep Learning\u003c\/i\u003e \u003c\/b\u003esets out the foundations of DL in a way that''s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You''ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you''re done, you''ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003cbr\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003cbr\u003e \u003c\/div\u003e","brand":"MediaPlace","offers":[{"title":"Default Title","offer_id":57312943702398,"sku":"NW9781617296482","price":51.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1379\/1261\/files\/9781617296482.jpg?v=1778590316","url":"https:\/\/mediaplace.com\/products\/math-architectures-of-deep-learning","provider":"MediaPlace","version":"1.0","type":"link"}