Krishnendu Chaudhury

Math and Architectures of Deep Learning

• Broschiertes Buch

Produktbeschreibung

Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. YouGÇÖll peer inside the GÇ£black boxGÇ¥ to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. YouGÇÖll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. YouGÇÖll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, youGÇÖll be glad you can quickly identify and fix problems.

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Produktdetails

• Verlag: Manning Publications
• Seitenzahl: 552
• Erscheinungstermin: 26. März 2024
• Englisch
• Abmessung: 234mm x 186mm x 33mm
• Gewicht: 996g
• ISBN-13: 9781617296482
• ISBN-10: 1617296481
• Artikelnr.: 63089099

Autorenporträt

Krishnendu Chaudhury is a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. He is presently CTO and co-founder of Drishti Technologies. He has a PhD in computer science from the University of Kentucky at Lexington.

Inhaltsangabe

table of contents
READ IN LIVEBOOK1AN OVERVIEW OF MACHINE LEARNING AND DEEP LEARNING
READ IN LIVEBOOK2INTRODUCTION TO VECTORS, MATRICES AND TENSORS FROM MACHINE
LEARNING AND DATA SCIENCE POINT OF VIEW
READ IN LIVEBOOK3INTRODUCTION TO VECTOR CALCULUS FROM MACHINE LEARNING
POINT OF VIEW
READ IN LIVEBOOK4LINEAR ALGEBRAIC TOOLS IN MACHINE LEARNING AND DATA
SCIENCE
READ IN LIVEBOOK5PROBABILITY DISTRIBUTIONS FOR MACHINE LEARNING AND DATA
SCIENCE
READ IN LIVEBOOK6BAYESIAN TOOLS FOR MACHINE LEARNING AND DATA SCIENCE
READ IN LIVEBOOK7FUNCTION APPROXIMATION: HOW NEURAL NETWORKS MODEL THE
WORLD
READ IN LIVEBOOK8TRAINING NEURAL NETWORKS: FORWARD AND BACKPROPAGATION
READ IN LIVEBOOK9LOSS, OPTIMIZATION AND REGULARIZATION
READ IN LIVEBOOK10ONE, TWO AND THREE DIMENSIONAL CONVOLUTION AND TRANSPOSED
CONVOLUTION IN NEURAL NETWORKS
11 IMAGE ANALYSIS: 2D CONVOLUTION BASED NEURAL NETWORK ARCHITECTURES FOR
OBJECT RECOGNITION AND DETECTION
12 VIDEO ANALYSIS: 3D CONVOLUTION BASED SPATIO TEMPORAL NEURAL NETWORK
ARCHITECTURES
READ IN LIVEBOOKAPPENDIX A: APPENDIX
A.1Dot Product and cosine of the angle between two vectors
A.2Computing variance of Gaussian Distribution
A.3Two Theorems in Statistic