September 17, 2016

Deploying Deep Learning at Scale for better data science and making inferences from data

In August, 2016, Intel is bolstering its artificial intelligence efforts by acquiring Nervana Systems for $400 million, a two-year-old startup considered among the leaders in developing machine learning technology.

In a video, Nervana's Naveen Rao discussed deep learning, a form of machine learning loosely inspired by the brain. Naveen explores the benefits of deep learning over other machine-learning techniques, recent advances in the field, the deep learning workflow, challenges in developing and deploying deep learning-based solutions, and the need for standardized tools for building and scaling deep learning solutions.

Convolutional Neural Nets are the main model. They are good for vision systems.

Recurrent Neural nets are good for modeling anything with time or sequence. Financial systems and Language models use RNN.

Stacked auto-encoders, Multi-layer perceptron and Deep belief networks are more fringe models.

A lot of the innovations in the next five years will come from the stacked auto-encoders in areas where we do not know what the objective is to start with.

Error rates for trained humans is 5% and now deep learning is at 3% for image and speech tasks.

Nervana used a python based framework for the problem definition

Nervana is in the oil and gas space and precision farming. They help determine how to farm better and find oil and gas better.

They are in the plant genomics space.

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