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Linkedin: https://www.linkedin.com/in/chris-dinant/
Kaggle: https://www.kaggle.com/chrisdinant
Github: https://www.kaggle.com/chrisdinant

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Kaggle Tensorflow Speech Recognition Challenge

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Implementation of the ResNet and CTC models at https://github.com/chrisdinant/speech In November of 2017 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! Exploratory Data Analysis The training data supplied by Google Brain consists of ca. 60,000 1-second-long .wav files in 32 directories that are named by the word spoken in the files. Only 10 of these are classes you need to identify, the others should go in the 'unknown' or 'silence' classes. There are a couple of things you can do to get a
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Post-Script to the Purpose of this Blog

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I said in my first post that I have this blog in the hope that it is useful to other people just starting to learn DataScience. This is not really to whole truth. The other reason, and perhaps more importantly, is that you learn so much from trying to explain things to other people. Who was it that said that you don't really understand something unless you can explain it to a five-year-old? Was it Feynman? That's really what I'm doing here. Making sure that I really understand the concepts and techniques that I'm learning and not just storming through course after course and book after book without the real end-goal in mind: become a better data scientist.
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What is Machine Learning?

When a computer learns from data it is called machine learning (ML). How do you teach a computer? You use ML algorithms. What is a machine learning algorithm? An ML algorithm is a function that takes in data and outputs a prediction. What kind of prediction? Predictions like, "This is a picture of a bicycle", or "Tomorrow it is going to rain", or "This customer will probably leave our company soon". Ok, so how does an algorithm do this? ML algorithms are designed to find attributes of datasets that describe groupings or trends within the data. This can be something simple as: "if a pixel in this location is blue you're looking at a smurf", or something complicated like: "a combination of this pixel intensity with this shape in this orientation here and this other shape there and so on, means this is a picture of Robert on a bicycle". Or another example: "if it rained yesterday and the air pressure is so and so and
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