Tyler Trine's repositories

MiniMax-AI-Project

A project for the AI class at University of Rochester.

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AlphaGo

A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search." Based off of the specification in the paper.

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fitbit_data

Scripts to continuously pull data from the fitbit API and push it to an SQL instance

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speed

Challenge to predict speed from video data

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temperature_predictions

Submissions for the temperature predictions problem on hackerrank.

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DeepMind-Atari-Deep-Q-Learner

The original code from the DeepMind article + my tweaks

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filter_manager

Template to get started using an external development environment for '1010 macro'

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IEEE-Quantum-Software-Development-Lab-Exercises

Labs and implemented solutions for the IEEE quantum software development course

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niftiest

Recommendation engine for niftyisland.io

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notion-py

Unofficial Python API client for Notion.so

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nupic

Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.

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qiskit

Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.

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qiskit-aer

Aer is a high performance simulator for quantum circuits that includes noise models

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qiskit-machine-learning

Quantum Machine Learning

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qiskit-terra

Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and algorithms.

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resume

Resume in LaTeX

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rm_sync

Mirror the contents of a Remarkable device to a Google Drive account and expose the folder structure via a Notion account.

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rmrl

Render reMarkable documents to PDF

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SpeakerInferenceReplication

A conceptual replication of Grodner and Sedivy's 2011 paper, The Effect of Speaker-Specific Information on Pragmatic Inferences. Protected information omitted.

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spotlight

Deep recommender models using PyTorch.

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SyntacticStockSentiment

This repo contains my experimentation with training custom sentiment models for the RNTN, as produced by the authors of this paper: http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf

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