  The proliferation of mobile devices, such as smartphones and Internet of
Things (IoT) gadgets, results in the recent mobile big data (MBD) era.
Collecting MBD is unprofitable unless suitable analytics and learning methods
are utilized for extracting meaningful information and hidden patterns from
data. This article presents an overview and brief tutorial of deep learning in
MBD analytics and discusses a scalable learning framework over Apache Spark.
Specifically, a distributed deep learning is executed as an iterative MapReduce
computing on many Spark workers. Each Spark worker learns a partial deep model
on a partition of the overall MBD, and a master deep model is then built by
averaging the parameters of all partial models. This Spark-based framework
speeds up the learning of deep models consisting of many hidden layers and
millions of parameters. We use a context-aware activity recognition application
with a real-world dataset containing millions of samples to validate our
framework and assess its speedup effectiveness.
