  This paper introduces a consistent estimator and rate of convergence for the
precision matrix of asset returns in large portfolios using a non-linear factor
model within the deep learning framework. Our estimator remains valid even in
low signal-to-noise ratio environments typical for financial markets and is
compatible with weak factors. Our theoretical analysis establishes uniform
bounds on expected estimation risk based on deep neural networks for an
expanding number of assets. Additionally, we provide a new consistent
data-dependent estimator of error covariance in deep neural networks. Our
models demonstrate superior accuracy in extensive simulations and the empirics.
