{"product_id":"math-and-architectures-of-deep-learning-9781617296482","title":"Math and Architectures of Deep Learning","description":"\u003cp\u003e • Author(s): Krishnendu Chaudhury\u003cbr\u003e • Publisher: Manning Publications\u003cbr\u003e • Publisher Imprint: Manning Publications\u003cbr\u003e • BISAC: Data Science - Machine Learning\u003c\/p\u003e\u003cp\u003e\u003cb\u003eShine a spotlight into the deep learning \"black box\". This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eInside \u003ci\u003eMath and Architectures of Deep Learning\u003c\/i\u003e you will find: \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMath, theory, and programming principles side by side\u003c\/li\u003e \u003cli\u003eLinear algebra, vector calculus and multivariate statistics for deep learning\u003c\/li\u003e \u003cli\u003eThe structure of neural networks\u003c\/li\u003e \u003cli\u003eImplementing deep learning architectures with Python and PyTorch\u003c\/li\u003e \u003cli\u003eTroubleshooting underperforming models\u003c\/li\u003e \u003cli\u003eWorking code samples in downloadable Jupyter notebooks\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003eThe mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. \u003ci\u003eMath and Architectures of Deep Learning\u003c\/i\u003e bridges 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. \u003cp\u003e\u003c\/p\u003e Foreword by Prith Banerjee. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Discover what's going on inside the black box! To work with deep learning you'll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you'll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eMath and Architectures of Deep Learning\u003c\/i\u003e teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You'll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe core design principles of neural networks\u003c\/li\u003e \u003cli\u003eImplementing deep learning with Python and PyTorch\u003c\/li\u003e \u003cli\u003eRegularizing and optimizing underperforming models\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Readers need to know Python and the basics of algebra and calculus. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eKrishnendu Chaudhury\u003c\/b\u003e is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 An overview of machine learning and deep learning\u003cbr\u003e 2 Vectors, matrices, and tensors in machine learning\u003cbr\u003e 3 Classifiers and vector calculus\u003cbr\u003e 4 Linear algebraic tools in machine learning\u003cbr\u003e 5 Probability distributions in machine learning\u003cbr\u003e 6 Bayesian tools for machine learning\u003cbr\u003e 7 Function approximation: How neural networks model the world\u003cbr\u003e 8 Training neural networks: Forward propagation and backpropagation\u003cbr\u003e 9 Loss, optimization, and regularization\u003cbr\u003e 10 Convolutions in neural networks\u003cbr\u003e 11 Neural networks for image classification and object detection\u003cbr\u003e 12 Manifolds, homeomorphism, and neural networks\u003cbr\u003e 13 Fully Bayes model parameter estimation\u003cbr\u003e 14 Latent space and generative modeling, autoencoders, and variational autoencoders\u003cbr\u003e A Appendix","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":45029306695831,"sku":"9781617296482","price":4946.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781617296482.webp?v=1769203652","url":"https:\/\/atlanticbooks.com\/products\/math-and-architectures-of-deep-learning-9781617296482","provider":"Atlantic Books","version":"1.0","type":"link"}