Shine 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.Inside Math and Architectures of Deep Learning you will find:Math, theory, and programming principles side by sideLinear algebra, vector calculus and multivariate statistics for deep learningThe structure of neural networksImplementing deep learning architectures with Python and PyTorchTroubleshooting underperforming modelsWorking code samples in downloadable Jupyter notebooksThe 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. Math and Architectures of Deep Learning 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.Foreword by Prith Banerjee.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyDiscover 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.About the bookMath and Architectures of Deep Learning 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.What's insideThe core design principles of neural networksImplementing deep learning with Python and PyTorchRegularizing and optimizing underperforming modelsAbout the readerReaders need to know Python and the basics of algebra and calculus.About the authorKrishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of ContentsAn overview of machine learning and deep learningVectors, matrices, and tensors in machine learningClassifiers and vector calculusLinear algebraic tools in machine learningProbability distributions in machine learningBayesian tools for machine learningFunction approximation: How neural networks model the worldTraining neural networks: Forward propagation and backpropagationLoss, optimization, and regularizationConvolutions in neural networksNeural networks for image classification and object detectionManifolds, homeomorphism, and neural networksFully Bayes model parameter estimationLatent space and generative modeling, autoencoders, and variational autoencodersA Appendix