Add missing examples to examples/README.md (#10637)

### Summary
This PR adds the missing examples to the list of examples in _examples/README.md_.

### PR Overview

- [n] This PR requires new unit tests [y/n] (make sure tests are included)
- [n] This PR requires to update the documentation [y/n] (make sure the docs are up-to-date)
- [y] This PR is backwards compatible [y/n]
- [n] This PR changes the current API [y/n] (all API changes need to be approved by fchollet)

examples/README.md

@@ -11,6 +11,9 @@ Trains a simple convnet on the MNIST dataset.
 [cifar10_cnn.py](cifar10_cnn.py)
 Trains a simple deep CNN on the CIFAR10 small images dataset.
 
+[cifar10_cnn_capsule.py](cifar10_cnn_capsule.py)
+Trains a simple CNN-Capsule Network on the CIFAR10 small images dataset.
+
 [cifar10_resnet.py](cifar10_resnet.py)
 Trains a ResNet on the CIFAR10 small images dataset.
 
@@ -33,7 +36,10 @@ Trains a Siamese multi-layer perceptron on pairs of digits from the MNIST datase
 Trains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset.
 
 [mnist_transfer_cnn.py](mnist_transfer_cnn.py)
-Transfer learning toy example.
+Transfer learning toy example on the MNIST dataset.
+
+[mnist_denoising_autoencoder.py](mnist_denoising_autoencoder.py)
+Trains a denoising autoencoder on the MNIST dataset.
 
 ----
 
@@ -66,6 +72,12 @@ Trains an LSTM model on the IMDB sentiment classification task.
 [lstm_stateful.py](lstm_stateful.py)
 Demonstrates how to use stateful RNNs to model long sequences efficiently.
 
+[lstm_seq2seq.py](lstm_seq2seq.py)
+Trains a basic character-level sequence-to-sequence model.
+
+[lstm_seq2seq_restore.py](lstm_seq2seq_restore.py)
+Restores a character-level sequence to sequence model from disk (saved by [lstm_seq2seq.py](lstm_seq2seq.py)) and uses it to generate predictions.
+
 [pretrained_word_embeddings.py](pretrained_word_embeddings.py)
 Loads pre-trained word embeddings (GloVe embeddings) into a frozen Keras Embedding layer, and uses it to train a text classification model on the 20 Newsgroup dataset.
 
@@ -120,4 +132,10 @@ Compares self-normalizing MLPs with regular MLPs.
 MNIST dataset with TFRecords, the standard TensorFlow data format.
 
 [mnist_dataset_api.py](mnist_dataset_api.py)
-MNIST dataset with TensorFlow's Dataset API.
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+MNIST dataset with TensorFlow's Dataset API.
+
+[cifar10_cnn_tfaugment2d.py](cifar10_cnn_tfaugment2d.py)
+Trains a simple deep CNN on the CIFAR10 small images dataset using Tensorflow internal augmentation APIs.
+
+[tensorboard_embeddings_mnist.py](tensorboard_embeddings_mnist.py)
+Trains a simple convnet on the MNIST dataset and embeds test data which can be later visualized using TensorBoard's Embedding Projector.
\ No newline at end of file