Math for deep learning: what you need to know to understand neural networks

By:

Kneusel, Ronald T

Publication details: No Starch Press, 2021. San Francisco:Description: xxv, 315p.; pbk; 24cmISBN:

9781718501904

Subject(s):

DDC classification:

006.310151 KNE

Summary: 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. https://nostarch.com/math-deep-learning

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	006.310151 KNE (Browse shelf(Opens below))	1	Available		031417

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Previous	006.31 YEJ Geometry of deep learning: a signal processing perspective	006.31 ZHE Feature engineering for machine learning: principles and techniques for data scientists	006.31015 MOI Algorithmic aspects of machine learning	006.310151 KNE Math for deep learning: what you need to know to understand neural networks	006.310151 NIE Essential math for data science: take control of your data with fundamental linear algebra, probability, and statistics	006.31015192 MUR Probabilistic machine learning: advanced topics	006.312 AGG Machine learning for text	Next

Includes index

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.

https://nostarch.com/math-deep-learning

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