Math for deep learning : what you need to know to understand neural networks / by Ronald T. Kneusel.
Material type:
TextPublication details: San Francisco: No starch press, 2022Description: xxv, 316 pages : illustrations ; 24 cmISBN: - 9781718501904
- 1718501900
- 006.310151 23 K689m
| Item type | Current library | Call number | Vol info | Copy number | Status | Barcode | |
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Library, Independent University, Bangladesh (IUB) Reference Stacks | 006.310151 K689m (Browse shelf(Opens below)) | 2022 | 01 | Not For Loan | 029478 | |
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Library, Independent University, Bangladesh (IUB) General Stacks | 006.310151 K689m (Browse shelf(Opens below)) | 2022 | 01 | Available | 029479 |
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| 006.31 H933 The Hundred - page machine learning book/ | 006.31 H933 The Hundred - page machine learning book/ | 006.31 K439m Machine learning : a probabilistic perspective / | 006.310151 K689m Math for deep learning : what you need to know to understand neural networks / | 006.31015192 M9781p Probabilistic machine learning : advanced topics / | 006.312 H2331d 2012 Data mining : concepts and techniques / | 006.312 M675c Clustering for Data Mining : a data recovery approach / |
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.
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