“…Learn the core mathematical concepts for machine learning and learn to implement them in R and Python About This Video Linear algebra notation is used in machine learning to describe the parameters and structure of different machine learning algorithms. …”

“…Learners should be familiar with basic deep learning concepts like the multilayer perceptron, linear algebra, Jupyter notebooks, and the basics of building and running TensorFlow programs. …”

“…Prerequisites include a basic understanding of linear algebra and calculus. Learn what deep learning neural networks are, what they're used for, and why they're powerful Discover the particular structure of neural networks and why it matters Explore the basic concepts used in building and training neural networks Develop a solid platform for learning more about deep learning and neural networks Laura Graesser is assisting with NVIDIA's autonomous driving project. …”

“…In this After Effects CS6 training course, expert trainer Jeff Sengstack introduces you to this powerful, non-linear visual effects software from Adobe. This tutorial is designed for the beginner, with no previous experience in After Effects required. …”

“…This second edition of Digital Optical Communications provides a comprehensive treatment of the modern aspects of coherent homodyne and self-coherent reception techniques using algorithms incorporated in digital signal processing (DSP) systems and DSP-based transmitters to overcome several linear and nonlinear transmission impairments and frequency mismatching between the local oscillator and the carrier, as well as clock recovery and cycle slips. …”

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“…Tillman and Cassone’s sophisticated simulation approach goes beyond conventional linear programming allocation of resources, helping decision-makers project inventory build-up on a shift-by-shift basis. …”

“…Reversing established critical approaches to the canon and literary influence, its contributors ask us to consider how reading against linear chronologies can elicit fascinating new patterns and perspectives. …”

“…With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. …”

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“…However, foods are highly complex systems in which: (i) Non-linear dynamics and interactions among different temporal and spatial scales must be considered; (ii) A wide range of physical phenomena occur; (iii) Different food matrices, with different microstructures and properties are involved; and (iv) The number of quality and safety indicators (such as bacteria, total volatile basic nitrogen, color, texture, odor, and sensory characteristics) is substantial. …”

“…The text covers historical accelerator development, transverse betatron motion, synchrotron motion, an introduction to linear accelerators, and synchrotron radiation phenomena in low emittance electron storage rings, introduction to special topics such as the free electron laser and the beam-beam interaction. …”

“…You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naṽe Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks…”

“…The fundamental thesis of the book is that the entrepreneurial process is not a linear progression from novel idea to successful innovation, but is an iterative series of experiments, where progress depends on the persistence and resilience of the individuals involved, and their ability and to learn from failure as well as success. …”

“…When Courant prepared the text of his 1942 address to the American Mathematical Society for publication, he added a two-page Appendix to illustrate how the variational methods first described by Lord Rayleigh could be put to wider use in potential theory. Choosing piecewise-linear approximants on a set of triangles which he called elements, he dashed off a couple of two-dimensional examples and the finite element method was born. ... …”

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“…As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. …”

“…Nicolas Kowalski and Axel Antoniotti outline how Criteo approached this issue, discussing how Criteo profiled its model to understand its bottleneck; why commonly shared solutions such as optimizing TensorFlow build for the target hardware, freezing and cleaning up the model, and using accelerated linear algebra (XLA) ended up being lackluster; and how Criteo rewrote is models from scratch, reimplementing cross-features and hashing functions using low-level TF operations in order to factorize as much as possible all TensorFlow nodes in its model."…”

“…Beginning with the earliest known finds, this volume provides a linear and thematic account of the history of the Old Stone Age, or Palaeolithic Period, covering major discoveries, interpretations and debates worldwide, a story that takes us from the embers of the Great Fire of London to the beginning of the Covid pandemic. …”