Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

This comprehensive guide is a must-read for anyone looking to master the mathematical concepts of deep learning.

With a focus on practical applications, you'll learn how to build neural networks from scratch, including linear neural networks and multilayer perceptrons.

The book covers essential topics such as linear algebra, eigenvalues and eigenvectors, singular value decomposition, gradient algorithms, regularization, multi-layered DL, forward propagation, optimization, backpropagation techniques, CNN, RNN, and GAN models.

By the end of this book, you'll have a strong foundation in neural networks and deep learning mathematical concepts, allowing you to confidently research and build custom models in DL.

Don't miss out on this opportunity to take your knowledge of deep learning to the next level!