Moreover, the exercises at the end of the chapter have been very well chosen for the intended level. 1.8), yet it is a good starting point for readers who would use the book as a self-study tool. The chapter is probably longer than what would be necessary, and some of the examples presented with a clarifying aim might not be a best choice (see, e.g., the example of the transformation between inertial frames of the electric and magnetic fields in Sec. Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. In this way, rather than defining tensors as multicomponent entities with a specific transformation law under a coordinate transformation, the concept is very appropriately introduced as a necessary requirement to represent physically meaningful quantities. More information about these requirements can be found here, you may have to. Then you will have the opportunity to practice what you learn with beginner. If you need to change your password, please comply with the TU/e requirements. Besides reviewing some basics in vector calculus, Chapter 1 explains very clearly how the need for physical quantities to have a tensor character arises. You will be introduced to ML and guided through deep learning using TensorFlow 2.0. Volume I begins with a brief discussion of algebraic structures followed by a rather detailed discussion of the algebra of vectors and tensors. Welcome to this tutorial on automatic differentiation. This classic introductory text, geared toward undergraduate students of. Automated differentiation tensor(True) Dynamic control flow is very common in deep learning. The book comprises eight chapters and may be ideally divided into two parts, with the first five chapters containing the core of the subject. To Volume 1 This work represents our effort to present the basic concepts of vector and tensor analysis. Buy a cheap copy of Elements of Tensor Calculus book by Andr Lichnerowicz.
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