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