Tryst with Deep Learning in International Data Science Game

Introduction Proof of the pudding lies in the eating. It takes working on the Deep network and witness it progressively produce good accuracy, to be truly amazed by the power of a Deep neural network. Using Deep Learning in a competitive environment requires all the more (as opposed to research) understanding of its strengths and costs. This is due to time boundedness and hence limited possibilities for experimentation. In this blog, we narrate our experience in using Deep Learning to achieve qualifying accuracy for a preliminary round of the International Data Science Game 2016. We are one of the 20 teams which qualified for the final round of the competition at Paris. We begin with a brief description of the problem statement, move through methodology and the implementation. We close the…


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