Math and Architectures of Deep Learning bridges the gap between theory and practice laying out
the math of deep learning side by side with practical implementations in Python and PyTorch.
YouGÇÖll peer inside the GÇ£black boxGÇ¥ to understand how your code is working and learn to
comprehend cutting-edge research you can turn into practical applications. Math and
Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to
working practitioners. Each chapter explores a new fundamental DL concept or architectural
pattern explaining the underpinning mathematics and demonstrating how they work in practice
with well-annotated Python code. YouGÇÖll 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. Learning mathematical foundations and neural network architecture can be challenging
but the payoff is big. YouGÇÖll be free from blind reliance on pre-packaged DL models and able
to build customize and re-architect for your specific needs. And when things go wrong
youGÇÖll be glad you can quickly identify and fix problems.
