Discover the practical aspects of implementing deep-learning solutions using the rich Python
ecosystem. This book bridges the gap between the academic state-of-the-art and the industry
state-of-the-practice by introducing you to deep learning frameworks such as Keras Theano and
Caffe. The practicalities of these frameworks is often acquired by practitioners by reading
source code manuals and posting questions on community forums which tends to be a slow and a
painful process. Deep Learning with Python allows you to ramp up to such practical know-how in
a short period of time and focus more on the domain models and algorithms. This book briefly
covers the mathematical prerequisites and fundamentals of deep learning making this book a
good starting point for software developers who want to get started in deep learning. A brief
survey of deep learning architectures is also included. Deep Learning with Python also
introduces you to key concepts of automatic differentiation and GPU computation which while
not central to deep learning are critical when it comes to conducting large scale experiments.
What You Will Learn Leverage deep learning frameworks in Python namely Keras Theano and
Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the
practical considerations of large scale experiments Take deep learning models to production Who
This Book Is For Software developers who want to try out deep learning as a practical solution
to a particular problem. Software developers in a data science team who want to take deep
learning models developed by data scientists to production.
