Math and Architectures of Deep Learning

about the technology

It''s important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You''ll 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, you''ll be glad you can quickly identify and fix problems.

about the book

Math and Architectures of Deep Learning sets out the foundations of DL in a way that''s both useful and accessible 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. 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.