Math for deep learning: what you need to know to understand neural networks
Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
San Francisco
No Starch Press
[2022]
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| Schlagworte: | |
| Zusammenfassung: | Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus – the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes’ theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You’ll ultimately put all this math to use as you explore and implement deep learning algorithms, including backpropagation and gradient descent – the foundational algorithms that have enabled the AI revolution. You’ll learn: •The rules of probability, probability distributions, and Bayesian probability •The use of statistics for understanding datasets and evaluating models •How to manipulate vectors and matrices, and use both to move data through a neural network •How to use linear algebra to implement principal component analysis and singular value decomposition •How to apply improved versions of gradient descent, like RMSprop, Adagrad and Adadelta Once you understand the core math concepts presented throughout this book through the lens of AI programming, you’ll have foundational know-how to easily follow and work with deep learning |
| Beschreibung: | xxiv, 316 Seiten Illustrationen, Diagramme |
| Format: | Mode of access: World Wide Web |
| ISBN: | 9781718501904 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T19:44:18Z |
| indexdate | 2024-07-10T09:31:23Z |
| institution | BVB |
| isbn | 9781718501904 |
| language | English |
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| spelling | Kneusel, Ronald T. Verfasser (DE-588)1071915738 aut Math for deep learning what you need to know to understand neural networks by Ronald T. Kneusel San Francisco No Starch Press [2022] xxiv, 316 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus – the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes’ theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You’ll ultimately put all this math to use as you explore and implement deep learning algorithms, including backpropagation and gradient descent – the foundational algorithms that have enabled the AI revolution. You’ll learn: •The rules of probability, probability distributions, and Bayesian probability •The use of statistics for understanding datasets and evaluating models •How to manipulate vectors and matrices, and use both to move data through a neural network •How to use linear algebra to implement principal component analysis and singular value decomposition •How to apply improved versions of gradient descent, like RMSprop, Adagrad and Adadelta Once you understand the core math concepts presented throughout this book through the lens of AI programming, you’ll have foundational know-how to easily follow and work with deep learning Mode of access: World Wide Web Deep learning (DE-588)1135597375 gnd rswk-swf Mathematik (DE-588)4037944-9 gnd rswk-swf Mathematik (DE-588)4037944-9 s Deep learning (DE-588)1135597375 s DE-604 Erscheint auch als Online-Ausgabe 978-1-7185-0191-1 |
| spellingShingle | Kneusel, Ronald T. Math for deep learning what you need to know to understand neural networks Deep learning (DE-588)1135597375 gnd Mathematik (DE-588)4037944-9 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4037944-9 |
| title | Math for deep learning what you need to know to understand neural networks |
| title_auth | Math for deep learning what you need to know to understand neural networks |
| title_exact_search | Math for deep learning what you need to know to understand neural networks |
| title_exact_search_txtP | Math for deep learning what you need to know to understand neural networks |
| title_full | Math for deep learning what you need to know to understand neural networks by Ronald T. Kneusel |
| title_fullStr | Math for deep learning what you need to know to understand neural networks by Ronald T. Kneusel |
| title_full_unstemmed | Math for deep learning what you need to know to understand neural networks by Ronald T. Kneusel |
| title_short | Math for deep learning |
| title_sort | math for deep learning what you need to know to understand neural networks |
| title_sub | what you need to know to understand neural networks |
| topic | Deep learning (DE-588)1135597375 gnd Mathematik (DE-588)4037944-9 gnd |
| topic_facet | Deep learning Mathematik |
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