Mathematical Foundations Guide to Neural Networks: CNNs, RNNs, LSTMs, Autoencoders, Attention Mechanisms, and More

By Alec Stovari

With clear explanations and detailed insights, in 650+ 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 NetworksBackpropagation and optimization techniquesConvolutional Neural Networks (CNNs) for image recognition and moreRecurrent Neural Networks (RNNs) and their sequential data processing powerLong Short-Term Memory (LSTM) networks for handling long-term dependenciesAutoencoders for dimensionality reduction and feature learningDropout and regularization techniques for robust modelsAttention mechanisms and transformer models revolutionizing NLPAdvanced deep learning architectures and real-world applicationsMathematical principles behind deep learning algorithms

This book serves as both an academic reference and a practical guide.

• Format: Paperback
• Language: English
• ISBN: B0DV9848PC
• ISBN13: 9798308301707
• Release Date: January 2025
• Publisher: Independently Published
• Length: 360 Pages
• Weight: 1.06 lbs.
• Dimensions: 0.8" x 6.0" x 9.0"