Update README.md

README.md

@@ -107,7 +107,7 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens
 - **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
 - **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
 - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.
-  - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
+- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
 - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.
 - **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset.
 - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.