ML Docs: Add t-SNE/UMAP notebook (read todo)

TODO: Add thumbnail, references, description of sections

Author: xhlu

doc/python/tsne-umap-projections.md

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+---
+jupyter:
+  jupytext:
+    notebook_metadata_filter: all
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.1'
+      jupytext_version: 1.1.1
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
+      version: 3
+    file_extension: .py
+    mimetype: text/x-python
+    name: python
+    nbconvert_exporter: python
+    pygments_lexer: ipython3
+    version: 3.7.6
+  plotly:
+    description: Visualize scikit-learn's k-Nearest Neighbors (kNN) classification
+      in Python with Plotly.
+    display_as: ai_ml
+    language: python
+    layout: base
+    name: t-SNE and UMAP projections
+    order: 1
+    page_type: example_index
+    permalink: python/t-sne-and-umap-projections/
+    thumbnail: thumbnail/tsne-umap-projections.png
+---
+
+## Basic t-SNE projections
+
+
+### Visualizing high-dimensional data with `px.scatter_matrix`
+
+```python
+import plotly.express as px
+
+df = px.data.iris()
+features = ["sepal_width", "sepal_length", "petal_width", "petal_length"]
+fig = px.scatter_matrix(df, dimensions=features, color="species")
+fig.show()
+```
+
+### Project data into 2D with t-SNE and `px.scatter`
+
+```python
+from sklearn.manifold import TSNE
+import plotly.express as px
+
+df = px.data.iris()
+
+features = df.loc[:, :'petal_width']
+
+tsne = TSNE(n_components=2, random_state=0)
+projections = tsne.fit_transform(features)
+
+fig = px.scatter(
+    projections, x=0, y=1, 
+    color=df.species, labels={'color': 'species'}
+)
+fig.show()
+```
+
+### Project data into 3D with t-SNE and `px.scatter_3d`
+
+```python
+from sklearn.manifold import TSNE
+import plotly.express as px
+
+df = px.data.iris()
+
+features = df.loc[:, :'petal_width']
+
+tsne = TSNE(n_components=3, random_state=0)
+projections = tsne.fit_transform(features, )
+
+fig = px.scatter_3d(
+    projections, x=0, y=1, z=2, 
+    color=df.species, labels={'color': 'species'}
+)
+fig.update_traces(marker_size=8)
+fig.show()
+```
+
+## Projections with UMAP
+
+Just like t-SNE, [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html) is a dimensionality reduction specifically designed for visualizing complex data in low dimensions (2D or 3D). As the number of data points increase, [UMAP becomes more time efficient](https://umap-learn.readthedocs.io/en/latest/benchmarking.html) compared to TSNE.
+
+In the example below, we see how easy it is to use UMAP as a drop-in replacement for scikit-learn's `manifold.TSNE`.
+
+```python
+from umap import UMAP
+import plotly.express as px
+
+df = px.data.iris()
+
+features = df.loc[:, :'petal_width']
+
+umap_2d = UMAP(n_components=2, init='random', random_state=0)
+umap_3d = UMAP(n_components=3, init='random', random_state=0)
+
+proj_2d = umap_2d.fit_transform(features)
+proj_3d = umap_3d.fit_transform(features)
+
+fig_2d = px.scatter(
+    proj_2d, x=0, y=1,
+    color=df.species, labels={'color': 'species'}
+)
+fig_3d = px.scatter_3d(
+    proj_3d, x=0, y=1, z=2, 
+    color=df.species, labels={'color': 'species'}
+)
+fig_3d.update_traces(marker_size=5)
+
+fig_2d.show()
+fig_3d.show()
+```
+
+## Visualizing image datasets
+
+In the following example, we show how to visualize large image datasets using UMAP. Here, we use [`load_digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html), a subset of the famous MNIST dataset that was downsized to 8x8 and flattened to 64 dimensions.
+
+```python
+import plotly.express as px
+from sklearn.datasets import load_digits
+from umap import UMAP
+
+digits = load_digits()
+
+umap_2d = UMAP(random_state=0)
+umap_2d.fit(digits.data)
+
+projections = umap_2d.transform(digits.data)
+
+fig = px.scatter(
+    projections, x=0, y=1, 
+    color=digits.target.astype(str), labels={'color': 'digit'}
+)
+fig.show()
+```
+
+### Reference