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We look at the structure of data and not its corresponding output Y. By looking at the structure of data we can better understand the given data. We can do this using various unsupervised machine learning algorithms, but in this case I am using an autoencoder to model the structure of the inputs.

Python 0.58% Jupyter Notebook 99.42%
autoencoder correlation-graphs neural-network school-assignment

unsupervised-learning's Introduction

  • 👋 Hi, I’m Harrison Gropper, but you can call me Harry if you'd like to!

  • 👀 I’m interested in anything that has to do with data science, and this is why: Data is the best story teller. As time passes, the more data there is and the more interesting stories we can tell.

  • 🌱 I’m currently learning Machine Learning. Implementing many algorithms from scratch and understanding how they work. For example, decision trees, gradient descent, autoencoders, and support vector machines.

  • 💞️ I’m looking to collaborate on anything that is data science related!

  • 📫 How to reach me: My email is [email protected]

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