  I study the price dynamics of non-fungible tokens (NFTs) and propose a deep
learning framework for dynamic valuation of NFTs. I use data from the Ethereum
blockchain and OpenSea to train a deep learning model on historical trades,
market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After
hyperparameter tuning, the model is able to predict the price of NFTs with high
accuracy. I propose an application framework for this model using
zero-knowledge machine learning (zkML) and discuss its potential use cases in
the context of decentralized finance (DeFi) applications.
