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Product Identifiers
PublisherNo Starch Press, Incorporated
ISBN-101718501900
ISBN-139781718501904
eBay Product ID (ePID)27050380222
Product Key Features
Number of Pages344 Pages
LanguageEnglish
Publication NameMath for Deep Learning : What You Need to Know to Understand Neural Networks
Publication Year2021
SubjectNeural Networks, General, Calculus
TypeTextbook
Subject AreaMathematics, Computers, Science
AuthorRonald T. Kneusel
FormatTrade Paperback
Dimensions
Item Height0.9 in
Item Weight23.2 Oz
Item Length9.1 in
Item Width7 in
Additional Product Features
Intended AudienceTrade
LCCN2021-939724
Reviews"What makes Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach." -Ed Scott, Ph.D., Solutions Architect & IT Enthusiast, "An excellent resource for anyone looking to gain a solid foundation in the mathematics underlying deep learning algorithms. The book is accessible, well-organized, and provides clear explanations and practical examples of key mathematical concepts. I highly recommend it to anyone interested in this field." --Daniel Gutierrez, insideBIGDATA "Ronald T. Kneusel has written a handy and compact guide to the mathematics of deep learning. It will be a well-worn reference for equations and algorithms for the student, scientist, and practitioner of neural networks and machine learning. Complete with equations, figures and even sample code in Python, this book is a wonderful mathematical introduction for the reader." --David S. Mazel, Senior Engineer, Regulus-Group "What makes Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach." --Ed Scott, Ph.D., Solutions Architect & IT Enthusiast
Dewey Edition23
IllustratedYes
Dewey Decimal006.310151
Table Of ContentIntroduction Chapter 1: Setting the Stage Chapter 2: Probability Chapter 3: More Probability Chapter 4: Statistics Chapter 5: Linear Algebra Chapter 6: More Linear Algebra Chapter 7: Differential Calculus Chapter 8: Matrix Calculus Chapter 9: Data Flow in Neural Networks Chapter 10: Backpropagation Chapter 11: Gradient Descent Appendix: Going Further
SynopsisMath 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., To truly understand the power of deep learning, you need to grasp the mathematical concepts that make it tick. Math for Deep Learning will give you a working knowledge of probability, statistics, linear algebra, and differential calculus-the essential math subfields required to practice deep learning successfully. Each subfield is explained with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. The book begins with fundamentals such as Bayes' theorem before progressing to more advanced concepts like training neural networks using vectors, matrices, and derivatives of functions. You'll then put all this math to use as you explore and implement backpropagation and gradient descent- the foundational algorithms that have enabled the Al revolution. You'll learn how to: Use statistics to understand datasets and evaluate models, Apply the rules of probability, Manipulate vectors and matrices to move data through a neural network, Use linear algebra to implement principal component analysis and singular value decomposition, Implement gradient-based optimization techniques like RMSprop, Adagrad, and Adadelta, The core math concepts presented in Math for Deep Learning will give you the foundation you need to unlock the potential of deep learning in your own applications. Book jacket., 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.
LC Classification NumberQ325.5
