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Developing various models such as Feedforward Neural Networks, Recurrent Neural Networks, Variational Autoencoders, and Boltzmann Machines.

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SeivenBell/Machine_Learning_for_Physics_Problems

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Severyn Balaniuk

In my Machine Learning for Physics module, I practiced developing various models such as Feedforward Neural Networks, Recurrent Neural Networks, Variational Autoencoders, and Boltzmann Machines.

For my final project, my team and I replicated a paper on the Trapped Ion programming of Ising Spin Models.

Using the Institute for Quantum Computing Framework, we generated ions and learned how to program them.

The whole project was built through Git version control, where we practiced creating, working in, and merging branches to simulate a real software development process.

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Developing various models such as Feedforward Neural Networks, Recurrent Neural Networks, Variational Autoencoders, and Boltzmann Machines.

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