Deep learning has broad applications in sentiment analysis, natural language understanding, computer vision, etc. The technology is growing at a breakneck speed on the back of rapid innovation. However, such innovations call for a higher number of parameters and resources. In other words, the model is as good as the metrics.
To that end, Google researcher Gaurav Menghani has published a paper on model efficiency. The survey covers the landscape of model efficiency from modelling techniques to hardware support. He proposed a method to make ‘deep learning models smaller, faster, and better’.
Menghani argues that while larger and more complicated models perform well on the tasks they are trained on, they may not show the same performance when applied to real-life situations.
Following are the challenges practitioners face while training and deploying models:
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