index.md

NumPy tutorials

This set of tutorials and educational materials is being developed in the numpy-tutorials repository, and is not a part of the NumPy source tree. The goal of this repository is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with. If you're interested in adding your own content, check the Contributing section.

To open a live version of the content, click the launch Binder button above. To open each of the .md files, right click and select "Open with -> Notebook". You can also launch individual tutorials on Binder by clicking on the rocket icon that appears in the upper-right corner of each tutorial. To download a local copy of the .ipynb files, you can either clone this repository or use the download icon in the upper-right corner of each tutorial.

---
hidden: true
---

features
applications
contributing

NumPy Features

{doc}`content/tutorial-svd`
^^^^^^^^^^^^^^^^^^^^^^^^^^^

```{glue:} thumb_svd
```

+++

{badge}`numpy.linalg, badge-primary`

---

{doc}`content/tutorial-ma`
^^^^^^^^^^^^^^^^^^^^^^^^^^

```{glue:} thumb_ma
```

+++

{badge}`numpy.ma, badge-primary`
{badge}`numpy.polynomial, badge-primary`

---

{doc}`content/save-load-arrays`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

![Default thumbnail: NumPy logo](_static/numpylogo.svg)

+++

{badge}`I/O, badge-primary`

NumPy Applications

{doc}`content/mooreslaw-tutorial`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

```{glue:} thumb_mooreslaw
```

---

{doc}`content/tutorial-deep-learning-on-mnist`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

```{glue:} thumb_mnist
```

---

{doc}`content/tutorial-deep-reinforcement-learning-with-pong-from-pixels`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

![Diagram showing the component operations of reinforcement learning detailed
in this tutorial](content/_static/tutorial-deep-reinforcement-learning-with-pong-from-pixels.png)

---

{doc}`content/tutorial-nlp-from-scratch`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

![Overview of the model architecture, showing a series of animated boxes.
There are five identical boxes labeled A and receiving as input one of the
words in the phrase "life's a box of chocolates". Each box is highlighted in
turn, representing the memory blocks of the LSTM network as information passes
through them, ultimately reaching a "Positive" output value.](content/_static/lstm.gif)

---

{doc}`content/tutorial-x-ray-image-processing`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

```{glue:} thumb_xray
```

---

{doc}`content/tutorial-static_equilibrium`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

```{glue:} thumb_static_eq
```

---

{doc}`content/tutorial-plotting-fractals`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

![An example of a fractal visualization from this tutorial](content/_static/fractal.png)

---

{doc}`content/tutorial-air-quality-analysis`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

![A grid showing the India Gate in smog above and clear air below](content/_static/11-delhi-aqi.jpg)

Useful links and resources

The following links may be useful:

• NumPy Code of Conduct
• Main NumPy documentation
• NumPy documentation team meeting notes
• NEP 44 - Restructuring the NumPy documentation
• Blog post - Documentation as a way to build Community

Note that regular documentation issues for NumPy can be found in the main NumPy repository (see the Documentation labels there).