Adding Interpretability in Deep Learning models

13:30-17:00, January 25

Workshop / Overview

Deep Learning models have achieved state-of-the-art results for various tasks in domains such as computer vision, natural language processing and machine translation to name a few.

Non-Linearity in deep learning models have enabled them with this success, but as a consequence these non-linearities have made them opaque or “black boxed”. While performance metrics such as precision vs recall, accuracy, auc-roc etc. may be important, but they aren’t sufficient to make deep learning models deployable in production workloads.

In many industries, such as finance, healthcare and government policy-making, where the cost of wrong predictions is high, it is important to earn trust by explaining how the model works.

This workshop aims to unravel the inner workings of deep learning  models,  while offering practical advice on how model predictions can be made explainable for several architectures that use Convolutions Neural Networks, Recurrent Neural Network, Attention, Self-Attention and discuss trade-off between interpretability and predictive power.

We’ll focus on three domains, namely – Computer Vision, Natural Language Processing, and TimeSeries forecasting and enabling data scientists to help them communicate how their models work in the real world. 

Workshop / Outcome

Practitioners will learn techniques to build and evaluate deep learning models, with particular emphasis on financial time series, NLP and CV use cases.

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • Intermediate knowledge of Python
  • Deep learning basics
  • Own laptop

Track / Co-organizers

Sunil Mallya

Principal Deep Learning Scientist, AWS

Delger Enkhbayar

Data Scientist, Amazon Web Services

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