Overview

Artificial Intelligence (AI) and Deep Learning (DL) have emerged as the most transformative technologies of the 21st century. From image recognition systems that can outperform human accuracy, to natural language processing (NLP) models that understand and generate human-like text, AI has become the foundation of modern innovations. At the heart of these advancements lies mathematics, and more specifically, the language of linear algebra, calculus, and tensors. For decades, vectors and matrices have served as the basic building blocks for machine learning algorithms. However, as data becomes increasingly complex, extending into multidimensional spaces, these tools often prove insufficient. A matrix can efficiently handle two-dimensional relationships, but when datasets span multiple dimensions-such as videos (spatial + temporal), multimodal AI (vision + audio + text), or biomedical imaging-traditional linear algebra falls short. This is where tensors come into play. Tensors are multidimensional generalizations of scalars, vectors, and matrices. They provide a natural mathematical representation for data that exist in more than two dimensions. Tensor calculus, therefore, becomes the mathematical engine that allows us to define, manipulate, and optimize these multidimensional structures in AI frameworks. Whether we are working with convolutions in neural networks, transformers in NLP, or tensor decompositions for dimensionality reduction, tensors are at the core of computation. This book-Tensor Calculus for AI and Deep Learning-is written with the purpose of bridging the gap between abstract tensor mathematics and its practical applications in AI frameworks such as TensorFlow, PyTorch, and JAX. It provides a deep, yet accessible, exploration of tensor calculus with a clear emphasis on how tensors empower modern AI systems. 2. Why This Book is Needed There are countless resources on machine learning, deep learning, and programming with frameworks. However, most of them treat tensors as black-box data structures without exploring their mathematical depth. Beginners often learn to ""use tensors"" in PyTorch or TensorFlow without fully understanding: What a tensor really is beyond just a multidimensional array. Why certain tensor operations behave the way they do (such as broadcasting, reshaping, or contractions). How tensor calculus naturally explains backpropagation, the backbone of neural network training. Where tensor decompositions contribute to dimensionality reduction and optimization in large-scale AI models. Without these insights, learners often remain framework-dependent rather than concept-driven, limiting their ability to innovate or optimize AI architectures. This book aims to change that. It combines mathematical rigor with practical coding examples, ensuring that readers not only know how to use tensors, but also why they work in the way they do. It is equally valuable for: Students who want to strengthen their mathematical foundations in AI. Researchers working on advanced machine learning models, quantum-inspired AI, or geometric deep learning. Practitioners seeking to optimize large models by understanding tensor decomposition and efficient tensor algebra. Educators looking for structured material to teach the connection between mathematics and deep learning.

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