Deep learning has revolutionized pattern recognition, introducing
tools that power a wide range of technologies in such diverse
fields as computer vision, natural language processing, and
automatic speech recognition. Applying deep learning requires you
to simultaneously understand how to cast a problem, the basic
mathematics of modeling, the algorithms for fitting your models to
data, and the engineering techniques to implement it all. This book
is a comprehensive resource that makes deep learning approachable,
while still providing sufficient technical depth to enable
engineers, scientists, and students to use deep learning in their
own work. No previous background in machine learning or deep
learning is required—every concept is explained from scratch and
the appendix provides a refresher on the mathematics needed.
Runnable code is featured throughout, allowing you to develop your
own intuition by putting key ideas into practice.
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