Chaudhury K. Math and Architectures of Deep Learning 2024 Final torrent

Textbook in PDF format

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.
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.
About the technology
Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective.
About the book
Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research.
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.
Table of Contents
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.
A Appendix