Precalculus Functions And Graphs 13th Edition
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Precalculus 1
The first half of an open textbook covering a two-quarter pre-calculus sequence including trigonometry. This first portion of the book is an investigation of functions, exploring the graphical behavior of, interpretation of, and solutions to problems involving linear, polynomial, rational, exponential, and logarithmic functions. An emphasis is placed on modeling and interpretation, as well as the important characteristics needed in calculus.
Math for Deep Learning
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
Precalculus: Functions and Graphs
PRECALCULUS: FUNCTIONS AND GRAPHS, 13th Edition, retains the features that have made it so popular: clear exposition, uncluttered layout and diverse, applications-rich examples and exercises. The excellent, time-tested problems have been widely praised for their consistency and appropriate level of difficulty for Precalculus students. Mathematically sound, PRECALCULUS: FUNCTIONS AND GRAPHS effectively prepares students for further courses in Mathematics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.