Ziyue Yang Ziyue Yang's Personal Website Fri, 21 Jan 2022 04:17:39 +0000 https://zyang.dev https://fetchrss.com https://zyang.dev https://www.google.com/s2/favicons?domain=https://zyang.dev Ziyue Yang https://twitter.com/yangzi33



(Feed generated with FetchRSS)]]> Fri, 21 Jan 2022 04:17:39 +0000 https://twitter.com/yangzi33 https://zyang.dev I'm a fourth-year undergraduate student at the University of Toronto, pursuing a degree in Statistics, Computer Science, and Mathematics. My interests intersect software engineering, machine learning, and data science.

Previously, I interned as an iOS developer under KWG Group Holdings at the Cohesion group, where I majorly worked with Swift and React Native. I led a workshop at Bigtheta, demonstrated software design principles and built web applications. I was involved in the LearnAI program at UofTAI during the academic year 2020/21.

I constantly seek opportunities, looking forward to making positive impacts and gaining valuable experience from the industry, while maintaining a healthy work-life balance.



(Feed generated with FetchRSS)]]> Fri, 21 Jan 2022 04:17:39 +0000 https://zyang.dev Microblog https://sigmahacks.org/ A microblogging web application written in Python and Flask, modified from the app that I originally built for SigmaHacks 3. Microblog has email support, user-following features, and I18n & L10n support. I am currently learning how to deploy it on Docker containers.

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(Feed generated with FetchRSS)]]> Fri, 21 Jan 2022 04:17:39 +0000 https://sigmahacks.org/ Optimal Pathfinder with Reinforcement Learning https://github.com/yangzi33/qlearning-maze-solver Software that discovers the optimal (shortest) path to a goal in a simulated maze environment. We implemented a Q learning algorithm that selects moves for the reinforcement learning agent. Exploration strategies include epsilon-greedy and the Boltzmann Stochastic Policy derived from the softmax of Q-values.

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(Feed generated with FetchRSS)]]> Fri, 21 Jan 2022 04:17:39 +0000 https://github.com/yangzi33/qlearning-maze-solver Neural Network for Facial Expression Classification https://github.com/yangzi33/face-nn Fully-connected neural networks trained on a subset of Toronto Faces Dataset. We implemented the neural network from scratch and trained it by tuning a set of hyperparameters, and we modified the model architecture for better generalization. The model achieved an excellent testing accuracy of 71.69%.

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(Feed generated with FetchRSS)]]> Fri, 21 Jan 2022 04:17:39 +0000 https://github.com/yangzi33/face-nn