Chaudhury Krishnendu, Ashok Ananya, Narumanchi Sujay, Shankar Devashish. Math and Architectures of Deep Learning

Manning Publications, 2024. — 553 p.
ISBN: 9781617296482.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 that 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.
Linear algebra, vector calculus, and multivariate statistics for deep learning.
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 wondering how those models 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.An overview of machine learning and deep learning.
Vectors, matrices, and tensors in machine learning.
Classifiers and vector calculus.
Linear algebraic tools in machine learning.
Probability distributions in machine learning.
Bayesian tools for machine learning.
Function approximation: How neural networks model the world.
Training neural networks: Forward propagation and backpropagation.
Loss, optimization, and regularization.
Convolutions in neural networks.
Neural networks for image classification and object detection.
Manifolds, homeomorphism, and neural networks.
Fully Bayes model parameter estimation.
Latent space and generative modeling, autoencoders, and variational autoencoders.