Hands-On Mathematics for deep learning.

Section 1: Essential Mathematics for Deep Learning
-1. Linear Algebra
-2. Vector Calculus
-3. Probability and Statistics
-4. Optimization
-5. Graph Theory
Section 2: Essential Neural Networks
-6. Linear Neural Networks
-7. Feedforward Neural Networks
-8. Regularization
-9. Convolutional Neural Networks
-10. Recurrent Neural Networks
Section 3: Advanced Deep Learning Concepts Simplified
-11. Attention Mechanisms
-12. Generative Models
-13. Transfer and Meta Learning
-14. Geometric Deep Learning
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Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.