Naive Bayes (NB) is ‘naive’ because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true. Here is why NB works anyway.

NB is a very intuitive classification algorithm. It asks the question, “*Given these features, does this measurement belong to class A or B?*”, and answers it by taking the proportion of all previous measurements with the same features belonging to class A multiplied by the proportion of all measurements in class A. If this number is bigger then the corresponding calculation for class B then…

From November 2017 to January 2018 the Google Brain team hosted a speech recognition challenge on Kaggle. The goal of this challenge was to write a program that can correctly identify one of 10 words being spoken in a one-second long audio file. Having just made up my mind to start seriously studying data science with the goal of turning a new corner in my career, I decided to tackle this as my first serious kaggle challenge.

In this post I will talk about ResNets, RNNs, 1D and 2D convolution, Connectionist Temporal Classification and more. Let’s go!

The training data…

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