Deep Learning: Notes

Mon, 27 Jul 2026 00:00:00 +0000

Deep Learning is to approximate the data distribution by learning the best-fit probability distribution from training data using a neural network.

Supervised Learning

What we have is a training set $\mathcal D_{\text{train}} = \{(x_i, y_i)\}_{i=1}^N$ sampled from $\mathcal D$. The empirical risk is defined as

$$R_{\text{emp}}(h) = \frac{1}{N} \sum_{i=1}^n L(h(x_i), y_i),$$

where $L$ is the loss function, $h$ is the hypothesis function in the hypothesis space $\mathcal H$. And the expected risk is defined as, can not be computed directly since we do not know the data distribution $\mathcal D$,

Deep Learning on Yukari's Blog

Deep Learning: Notes

Supervised Learning