  Existing theories on deep nonparametric regression have shown that when the
input data lie on a low-dimensional manifold, deep neural networks can adapt to
the intrinsic data structures. In real world applications, such an assumption
of data lying exactly on a low dimensional manifold is stringent. This paper
introduces a relaxed assumption that the input data are concentrated around a
subset of $\mathbb{R}^d$ denoted by $\mathcal{S}$, and the intrinsic dimension
of $\mathcal{S}$ can be characterized by a new complexity notation -- effective
Minkowski dimension. We prove that, the sample complexity of deep nonparametric
regression only depends on the effective Minkowski dimension of $\mathcal{S}$
denoted by $p$. We further illustrate our theoretical findings by considering
nonparametric regression with an anisotropic Gaussian random design
$N(0,\Sigma)$, where $\Sigma$ is full rank. When the eigenvalues of $\Sigma$
have an exponential or polynomial decay, the effective Minkowski dimension of
such an Gaussian random design is $p=\mathcal{O}(\sqrt{\log n})$ or
$p=\mathcal{O}(n^\gamma)$, respectively, where $n$ is the sample size and
$\gamma\in(0,1)$ is a small constant depending on the polynomial decay rate.
Our theory shows that, when the manifold assumption does not hold, deep neural
networks can still adapt to the effective Minkowski dimension of the data, and
circumvent the curse of the ambient dimensionality for moderate sample sizes.
