# Math and Architectures of Deep Learning

This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. You can save 40% off Math and Architectures of Deep Learning until May 13! Just enter the code nlkdarch40 at checkout when you buy from manning.com.

If you’re a deep learning engineer, it can be hard to stay on the cutting edge of your field. All the coolest and most exciting research tends to be published in complex and hard-to-access academic papers, using a level of math and theory that goes sailing over your head. If you’re not careful, you’ll find yourself with a blind reliance on prepackaged deep learning models, never really understanding what’s going on in your own algorithms.

Math and Architectures of Deep Learning is here to help you out. This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big: when things go wrong (and let’s be honest, they always go wrong) you’ll know exactly how to fix them, rather than relying on costly trial and error.

The book is written by Dr Krishnendu Chaudhury, a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you’re done, you’ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.

You can save 40% off Math and Architectures of Deep Learning until May 13!

Just enter the code **nlkdarch40** at checkout when you buy from manning.com.