[MRG] Update examples in the doc (#359)

* add transparent color logo

* add transparent color logo

* move screenkhorn

* move stochastic and install ffmpeg on circleci

* try something

* add sudo

* install ffmpeg before python

* cleanup examples

* test svg scrapper

* add animation for reg path

* better example OT sivergence

* update ttles and add plots

* update free support

* proper figure indexes

* have less frame sin animation

* update readme and release file

* add tests for python 3.10

1 files changed, 6 insertions, 4 deletions

diff --git a/README.md b/README.md
index ec5d221..0c3bd19 100644
--- a/README.md
+++ b/README.md
@@ -29,8 +29,11 @@ POT provides the following generic OT solvers (links to examples):
 * Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
 * [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html)  (exact [13] and regularized [12]), differentiable using gradients from
  * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24]
-* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
-* [Stochastic solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
+* [Stochastic
+  solver](https://pythonot.github.io/auto_examples/others/plot_stochastic.html) and
+  [differentiable losses](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html) for
+  Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
+* [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
 * Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
 * [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25].
 * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
@@ -119,7 +122,7 @@ Note that for easier access the module is named `ot` instead of `pot`.
 
 ### Dependencies
 
-Some sub-modules require additional dependences which are discussed below
+Some sub-modules require additional dependencies which are discussed below
 
 * **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
 
@@ -127,7 +130,6 @@ Some sub-modules require additional dependences which are discussed below
 pip install pymanopt autograd
 ```
 
-* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). Obviously you will need CUDA installed and a compatible GPU. Note that this module is deprecated since version 0.8 and will be deleted in the future. GPU is now handled automatically through the backends and several solver already can run on GPU using the Pytorch backend.
 
 ## Examples