Creating Slides
===============

We can mark a notebook and then create slides from that notebook. For
example, here is the generate
`slides <https://nbviewer.jupyter.org/format/slides/github/d2l-ai/d2l-pytorch-slides/blob/main/chapter_preliminaries/ndarray.ipynb#/>`__
from the markdown `source
file <https://github.com/d2l-ai/d2l-en/blob/master/chapter_preliminaries/ndarray.md>`__.
Let explain how to do it by the following example. It's a markdown file
with marks to generate slides.

.. raw:: latex

   \diilbookstyleinputcell

.. code:: md

    # Data Manipulation

    ## Getting Started

    To start, we can use `arange` to create a row vector `x`
    containing the first 12 integers starting with 0,
    though they are created as floats by default.

    A tensor represents a (possibly multi-dimensional) array of numerical values. We can access a tensor's *shape*.


    ```{.python .input}
    import numpy as np

    x = np.arange(12)
    x
    ```

    Many more operations can be applied elementwise,
    including unary operators like exponentiation.


    ```{.python .input}
    np.exp(x)
    ```

    Even when shapes differ, we can still perform elementwise operations
    by invoking the *broadcasting mechanism*.


    ```{.python .input}
    a = np.arange(3).reshape(3, 1)
    b = np.arange(2).reshape(1, 2)
    a, b
    ```

The above code block will generate 2 slides. The first slide contains
the following contents:

.. raw:: latex

   \diilbookstyleinputcell

.. code:: md

    # Data Manipulation

    A tensor represents a (possibly multi-dimensional) array of numerical values. We can access a tensor's *shape*.

    ```{.python .input}
    import numpy as np

    x = np.arange(12)
    x
    ```

You can see that we automatically copied the level-1 heading and the
code block. In addition, we copied the text between ``and``, while
dropped all others.

The second slide contains the following:

.. raw:: latex

   \diilbookstyleinputcell

.. code:: md

    Many operations can be applied elementwise,
    e.g. `exp`

    ```{.python .input}
    np.exp(x)
    ```

    Even when shapes differ, we can still perform elementwise operations

    ```{.python .input}
    a = np.arange(3).reshape(3, 1)
    b = np.arange(2).reshape(1, 2)
    a, b
    ```

First you can see is that all text between these three paris (``,``),
(``,``), and
(\```) are kept. Here``\ [``means starting a new slide, while``\ (``means continuing the current slide. (Level-1 heading will start a new slide, so we used``\ (``in the previous block). In addition,``\ ~~\`
means the text will only appear in slides, why not in the normal
notebooks, htmls or pdfs.

Second, we didn't start a new slide before the last code block, i.e.
there is no level-1 heading and no (``,``) pair, so the last two code
blocks are merged into the same slide.