Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.
Inside Math and Architectures of Deep Learning you will find:
Math, theory, and programming principles side by side The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. 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. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.
Foreword by Prith Banerjee.
About the technology
About the book
What’s inside
The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader
Readers need to know Python and the basics of algebra and calculus.
About the author
Table of Contents
1 An overview of machine learning and deep learning
2 Vectors, matrices, and tensors in machine learning
3 Classifiers and vector calculus
4 Linear algebraic tools in machine learning
5 Probability distributions in machine learning
6 Bayesian tools for machine learning
7 Function approximation: How neural networks model the world
8 Training neural networks: Forward propagation and backpropagation
9 Loss, optimization, and regularization
10 Convolutions in neural networks
11 Neural networks for image classification and object detection
12 Manifolds, homeomorphism, and neural networks
13 Fully Bayes model parameter estimation
A Appendix
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