DEEP LEARNING

IOT principles

1) Data Science for IoT is based on mostly time series data from IoT devices – but with three additional techniques: Deep learning, Sensor fusion and Streaming.

2) We consider Deep learning because we treat cameras as sensors but also include reinforcement neural networks for IoT devices

3) The course is based on templates(code) for the above in R, Python and Spark(Scala). It is hence suited for people with a Programming background(even if from other languages)

4) The ideas learnt in the core modules are implemented in Projects. Projects could last as long as six months5) Much of our work has been published in leading blogs like KDnuggets and Data Science Central etc6) The course has evolved based on active participation from participants: ex Jean Jacques

Barnard(methodology), Peter Marriot(Python), Sibanjan Das(H2O/Deep learning), Shiva Soleimani(methodology), Yongkang Gao(Nvidia TK1), Raj Chandrasekaran(Spark) , Vinay Mendiratta(systems level optimization of IoT sensors). We plan to open source most of our code

7) We use Apache Spark for Streaming and Apache flink for sensor fusion. 8) Ironically, due to the emphasis on Data, the course is strictly not an IoT course ie we are concerned only with

applying predictive learning algorithms on IoT datasetsInterested ? Email info@futuretext.com for details of the September batch (now in it’s fourth batch)

PROJECTSSENSOR FUSION

STREAMING

CODE IMPLEMENTATIONS (R, SPARK, PYTHON)

AND METHODOLOGY IE PROBLEM SOLVING FOR IOT ANALYTICS

Statistics foundations

Time Series

Spark

Data SciencePrinciples

NOQL databases