{"product_id":"matrix-computations-for-deep-learning-anshuman-mishra-9798262315116","title":"Matrix computations for deep learning: Foundations of svd tensor operations and cnns","description":"In the rapidly growing field of artificial intelligence (AI) and machine learning (ML), the role of mathematics-particularly linear algebra and matrix computations-cannot be overstated. Every neural network, from the simplest perceptron to the most advanced convolutional neural network (CNN) or transformer model, is fundamentally built upon matrix and tensor operations. While researchers and engineers often interact with these operations indirectly through deep learning frameworks such as TensorFlow, PyTorch, or JAX, the efficiency, interpretability, and scalability of these systems depend directly on a deep understanding of matrix computations.\u003cbr\u003eThe book \u003cb\u003e\"Matrix Computations for Deep Learning\"\u003c\/b\u003e is written with the goal of bridging the gap between the theoretical foundations of matrix algebra and the applied techniques in deep learning. By focusing on singular value decomposition (SVD), tensor operations, and convolutional neural network foundations, this book provides students, researchers, and industry professionals with both the \u003cb\u003econceptual clarity\u003c\/b\u003e and the \u003cb\u003epractical skills\u003c\/b\u003e necessary to design, implement, and optimize modern AI systems. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhy This Book is Needed\u003c\/b\u003e\u003cbr\u003eIn most existing textbooks on deep learning, matrix computations are introduced briefly as a background requirement, often summarized in one or two introductory chapters. While this approach may provide enough to begin coding neural networks, it leaves a gap in understanding how these computations actually shape model performance, stability, and scalability.\u003cbr\u003eFor example: \u003cul\u003e\n\u003cli\u003eSingular Value Decomposition (SVD) is not just a mathematical trick; it is at the heart of data compression, dimensionality reduction, and optimization in deep learning.\u003c\/li\u003e\n\u003cli\u003eTensor decompositions are not merely advanced algebraic tools; they enable model compression, multi-modal learning, and scalable architectures for big data.\u003c\/li\u003e\n\u003cli\u003eConvolutions, the backbone of CNNs, are more than a \"sliding filter\" - they can be fully understood as structured matrix multiplications that connect directly to Fourier transforms and wavelets.\u003c\/li\u003e\n\u003c\/ul\u003eThis book is therefore \u003cb\u003enot just about theory or coding\u003c\/b\u003e, but about creating a \u003cb\u003edeep mathematical intuition\u003c\/b\u003e while always keeping in mind the \u003cb\u003epractical applications in deep learning\u003c\/b\u003e. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eHow This Book is Structured\u003c\/b\u003e\u003cbr\u003eThe book is divided into six major parts: \u003col\u003e\n\u003cli\u003e\n\u003cb\u003eFoundations of Matrix Computations\u003c\/b\u003e - covering linear algebra basics, vector spaces, and norms that are directly applied in neural network optimization.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eMatrix Decompositions\u003c\/b\u003e - exploring SVD, QR, LU, and eigenvalue decompositions with applications in dimensionality reduction, regularization, and optimization.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTensor Operations\u003c\/b\u003e - moving beyond matrices to higher-order tensors, tensor decompositions, and computational efficiency in frameworks like PyTorch and TensorFlow.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eMatrix Computations for CNNs\u003c\/b\u003e - showing how convolutions, pooling, and backpropagation can be represented entirely through structured matrix operations.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eApplications and Advanced Topics\u003c\/b\u003e - linking matrix methods with dimensionality reduction, computer vision, and large-scale AI systems.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePractical Implementations\u003c\/b\u003e - providing hands-on coding examples in Python, with an emphasis on efficiency, stability, and scalability.\u003c\/li\u003e\n\u003c\/ol\u003eEach chapter contains \u003cb\u003emathematical explanations, graphical illustrations, step-by-step derivations, and code snippets\u003c\/b\u003e, ensuring that readers not only understand the concepts but also see how they are implemented in practice. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhy This Book is Important for Study\u003c\/b\u003e\u003cbr\u003e1. Building Mathematical Intuition for Deep Learning\u003cbr\u003eMatrix computations are the foundation upon which deep learning is built. Without a solid grasp of these operations,\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Anshuman Mishra\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798262315116\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/26\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 280\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.44lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 11.00h x 8.50w x 0.59d","brand":"Anshuman Mishra","offers":[{"title":"Paperback","offer_id":47520838942975,"sku":"9798262315116","price":18.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_8f76af2d-0a76-4dac-bd15-e6f18a10d96e.jpg?v=1762887316","url":"https:\/\/www.whiterainbookhouse.com\/products\/matrix-computations-for-deep-learning-anshuman-mishra-9798262315116","provider":"WR Book House","version":"1.0","type":"link"}