# AsciiDoc Example

## Table (Playing with Kaggle; uses AsciiDoc includes)

--- snip ---

Model Epochs bs lr Momentum Result (local) Result (Kaggle) Remarks

SimpleNet

50

20

0.007

0.9

~97

ConvNet

50

25

0.008

0.9

99.257

"

50

17

0.008

0.9

99.1964

augmented

"

50

17

0.008

0.9

99.3143

99.342

augmented bn

Binary Ensemble

25

17

0.007

0.9

>99

"

22

17

0.0085

0.9

99.23928

99.328

augmented

"

22

17

0.0085

0.9

99.34643

99.357

augmented bn

--- snap ---

(1) augmented: one additional variant per image rotated randomly in [-5,5] degree.
(2) bn: batch-norm

## Mathematics

Inline Math: $f_{i}(x) = \int_{\tau(\pi)}^\infty e(x,y)dy$

Display Math:

$\begin{bmatrix} a_{11} & a_{12} & \dots \\ \vdots & \ddots & \\ a_{n1} & & a_{nn} \end{bmatrix}$

Inline Math: $(\Omega,\mathcal{F},P) \coloneqq (\Omega^+ \times \Omega^-,\mathcal{F^+ }\otimes\mathcal{F^- },P^+ \times P^- )$

Display Math:

The Wiener process in $(\Omega,\mathcal{F},P)$ is defined by

$\omega(t) \coloneqq \left\{ \begin{array}{cc} (\omega^+(t),0) & t\geq 0 \\ (0,\omega^-(t)) & t<0 \end{array} \right.$

More math…​

\tag {3.1a} \begin{aligned} dy_t &= \sum_{i=1}^n \frac{x_t^i}{\|x_t\|^2} \left( \sum_{j=1}^n u_{ij} x_t^i\,dt + \sum_{k=1}^m \sum_{j=1}^n v_{ij}^k x_t^j\circ dW_t^k\right) + \cdots\\ \cdots &+ \frac{1}{2} \sum_{i,j}^n \left( \frac{\delta_{ij}}{\|x_t\|^2} - \frac{2x_t^i x_t^j}{\|x_t\|^4} \right)\; \sum_k^m \sum_{l,p}^n v_{il}^k v_{jp}^k\, x_t^l x_t^p\;dt \end{aligned}

With $z_t \coloneqq \frac{x_t}{\|x_t\|}$ we get the following differential equation on the unit sphere:

\tag{3.1b} \begin{aligned} y_t &= y_0 + \int_0^t z_t^TUz_t- \|z_t\|^{-2} z_t^T\hat{V}\hat{V}^T z_t + \frac{1}{2}\text{ trace }(\hat{V}\hat{V}^T)\,dt +\cdots\\ \cdots &+ \sum_{k=1}^m \int_0^t z_t^TV^kz_t\circ dW_t^k \end{aligned}

### Outline as partial TeX file inclusion

Line block - takes only the inner part of a LaTeX display-math environment by specifying row delimiters for the included LaTeX file:

--- snip ---

\begin{aligned} z &= z(x, y)\\ x &= x(s_1, s_2)\\ y &= y(t_1, t_2)\\ s_i &= s_i(w) \; \forall i \in \{1,2\} \\ t_i &= t_i(w) \; \forall i \in \{1,2\} \end{aligned}

--- snap ---

Single line (as inline): start include → ${\partial z\over\partial w}={\partial z\over\partial x}\cdot\Bigg({\partial x\over\partial s_1}\cdot{\partial s_1\over\partial w} + {\partial x\over\partial s_2}\cdot{\partial s_2\over\partial w}\Bigg) + {\partial z\over\partial y}\cdot\Bigg({\partial y\over\partial t_1}\cdot{\partial t_1\over\partial w} + {\partial y\over\partial t_2}\cdot{\partial t_2\over\partial w}\Bigg)$ ← stop include

## Footnotes

Footnotes are possible, like using  and .

## SVG image Caption (Neuron image - 50%)

## Code

### Verbatim file inclusion

# include example

from torch.utils.data import TensorDataset
from torch import Tensor, LongTensor, FloatTensor

'''
Load data from kaggle mnist set.
'''
df = pd.read_csv(str(path))  # 40.000 entries
# tdata = pd.read_csv(data_raw_dir + sep + 'train.csv') # 28.000 entries

has_labels = True if 'label' in df.columns else False

### Normal code block

from torch.utils.data import TensorDataset
from torch import Tensor, LongTensor, FloatTensor

'''
Load data from kaggle mnist set.

path -- input csv

Return scaled images [0,1] and labels (if available)
as numpy arrays (dtype: float32, int64)
'''
df = pd.read_csv(str(path))  # 40.000 entries
# tdata = pd.read_csv(data_raw_dir + sep + 'train.csv') # 28.000 entries

has_labels = True if 'label' in df.columns else False

Some inline code.

1. First footnote
2. Second footnote
3. Interesting in terms of content, by the way

## References

Giovanni Dematteis, Tobias Grafke, and Eric Vanden-Eijnden. Rogue waves and large deviations in deep sea. Proceedings of the National Academy of Sciences, 115(5):855–860, January 2018. doi:10.1073/pnas.1710670115.

Hugo Touchette. A basic introduction to large deviations: Theory, applications, simulations. arXiv:1106.4146 [cond-mat, physics:math-ph], February 2012. arXiv:1106.4146.