chore: training example

docs/built-in-models/linear.md

@@ -1,10 +1,10 @@
 # Linear models
 
-This document introduces some [scikit-learn](https://scikit-learn.org/stable/)'s linear models for `regression` and `classification` that Concrete ML provides.
+This page explains Concrete ML linear models for both classification and regression. These models are based on [scikit-learn](https://scikit-learn.org/stable/) linear models.
 
-## Supported models
+## Supported models for encrypted inference
 
-The use of the following models in FHE is very similar to the use of scikit-learn's [API](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model). They are also compatible with some of scikit-learn's main workflows, such as `Pipeline()` and `GridSearch()`.
+The following models are supported for training on clear data and predicting on encrypted data. Their API is similar the one of [scikit-learn](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model). These models are also compatible with some of scikit-learn's main workflows, such as `Pipeline()` and `GridSearch()`.
 
 |                                             Concrete ML                                              |                                                                         scikit-learn                                                                         |
 | :--------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: |
@@ -20,6 +20,12 @@ The use of the following models in FHE is very similar to the use of scikit-lear
 |         [ElasticNet](../references/api/concrete.ml.sklearn.linear_model.md#class-elasticnet)         |             [ElasticNet](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet)             |
 |       [SGDRegressor](../references/api/concrete.ml.sklearn.linear_model.md#class-sgdregressor)       |                           [SGDRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html)                           |
 
+## Supported models for encrypted training
+
+In addition to predicting on encrypted data, the following models  support training on encrypted data.
+
+|       [SGDClassifier](../references/api/concrete.ml.sklearn.linear_model.md#class-sgdclassifier)       |                           [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html)                           |
+
 ## Quantization parameters
 
 The `n_bits` parameter controls the bit-width of the inputs and weights of the linear models. Linear models do not use table lookups and thus alllows weight and inputs to be high precision integers.

docs/tutorials/ml_examples.md

@@ -67,3 +67,9 @@ Two different configurations of the built-in, fully-connected neural networks ar
 - [Regressor comparison](../advanced_examples/RegressorComparison.ipynb)
 
 Based on three different synthetic data-sets, all the built-in classifiers are demonstrated in this notebook, showing accuracies, inference times, accumulator bit-widths, and decision boundaries.
+
+### 7. Training on encrypted data
+
+- [LogisticRegression training](../advanced_examples/LogisticRegressionTraining.ipynb)
+
+This example shows how to configure a training algorithm that works on encrypted data and how to deploy it in a client/server application.