Math for deep learning : what you need to know to understand neural networks / by Ronald T. Kneusel.
By: Kneusel, Ronald T [author.].
Publisher: San Francisco : No Starch Press, ©2022Description: xxv, 316 pages : illustrations ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781718501904.Subject(s): Machine learning -- Mathematics | Neural networks (Computer science) -- Mathematics | Neural networks (Computer science) -- MathematicsGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | Q325.5 .K545 2022 (Browse shelf) | Available | AU00000000019543 |
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| Q325.5 .K454 2019 Deep learning / | Q325.5 .K455 2015 Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / | Q325.5 .K455 2020 Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / | Q325.5 .K545 2022 Math for deep learning : what you need to know to understand neural networks / | Q325.5 .K55 2021 Practical deep learning : a Python-based introduction / | Q325.5 .K568 2018 Machine learning : a concise introduction / | Q325.5 .K57 2021 Machine learning and deep learning using Python and Tensorflow / |
Includes bibliographical references and index.
Setting the stage -- Probability -- More probability -- Statistics -- Linear algebra -- More linear algebra -- Differential calculus -- Matrix calculus -- Data flow in neural networks -- Backpropagation -- Gradient descent -- Going further.
To truly understand the power of deel 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 AI revolution.