Welcome to Manning Early Access Program (MEAP) for “Math and Architectures of Deep Learning”. This membership will give you access to the developing manuscript along with the resources which includes fully functional python/PyTorch code downloadable and executable via Jupyter-notebook.
Deep learning is a complex subject. On one hand, it is deeply theoretical with extensive mathematical backing. Indeed, without a good intuitive understanding of the mathematical underpinnings, one is doomed to merely running off the shelf pre-packaged models without understanding them fully. These models often do not lend themselves well to the exact problem one needs to solve and one is helpless if any change or re-architecting is necessary. On the other hand, deep learning is also intensely practical requiring significant Python programming skills on new platforms like Tensorflow and PyTorch. Failure to master those leaves one unable to solve any real problem.
This author feels that there is a dearth of books that addresses both of these aspects of the subject in a connected fashion. That is what has led to the genesis of this book.
The author will feel justified in his efforts if these pages help the reader to become a successful exponent in the art and science of deep learning.