Stovari A. Mathematical Foundations Guide to Neural Networks...2025 torrent

Textbook in PDF format

With clear explanations and detailed insights, in 170+ pages, you will learn the inner workings of backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). The book also dives into advanced techniques such as dropout, autoencoders, and attention layers that are transforming the AI landscape. Dive deep into the theory behind each model, understand their applications, and master the mathematics that power modern Machine Learning.
Key Topics Covered
The theoretical foundations of Neural Networks
Backpropagation and optimization techniques
Convolutional Neural Networks (CNNs) for image recognition and more
Recurrent Neural Networks (RNNs) and their sequential data processing power
Long Short-Term Memory (LSTM) networks for handling long-term dependencies
Autoencoders for dimensionality reduction and feature learning
Dropout and regularization techniques for robust models
Attention mechanisms and transformer models revolutionizing NLP
Advanced Deep Learning architectures and real-world applications
Mathematical principles behind Deep Learning algorithms
This book serves as both an academic reference and a practical guide.
Preface
Matrix algebra and numerical methods
Interpolation
Iterations and stability review
Fixed Point Iterative Methods
Iterative solving of systems of linear equations
Optimization
Numerical integration
Numerical differentiation and solving differential equations