readme update

README.md

@@ -13,7 +13,7 @@
 ## What is Q-RNN? 🤔
 ![Random Neuron](images/random_neuron.png)
 
-(Hossam Abdelbaki (2020). rnnsimv2.zip (https://www.mathworks.com/matlabcentral/fileexchange/91-rnnsimv2-zip), MATLAB Central File Exchange. Retrieved September 22, 2020.)
+(Look of a Random Neuron [4])
 
 It is a compose of Simple RNN and [Random Neural Network](https://github.com/bilkosem/random_neural_network). Queueing RNN uses the fundamental math of Queueing Theory and G-Queues while combining it with the powerful architecture of Recurrent Neural Networks. For more detailed explanation about the theoretical background of QRNN check the [mathematical-model](https://github.com/bilkosem/queueing-rnn/tree/master/mathematical-model) folder, and references section. 
 
@@ -23,7 +23,7 @@ In order to evaluate the performance of QRNN, it has been compared with LSTM, GR
 
 ![Overall Comparison](test_results/overall_comparison.png)
 
-For more detailed results check the [test_results](https://github.com/bilkosem/queueing-rnn/tree/master/test_results) folder 🔎.
+Check the [test_results](https://github.com/bilkosem/queueing-rnn/tree/master/test_results) folder to see detailed results 🔎.
 
 ## Installation 🛠
 
@@ -45,17 +45,25 @@ python setup.py install
 
 ```python
 from queueing_rnn import QRNN
-```
 
+data=data.reshape((samples,timesteps,features))
+qrnn = QRNN([features, hidden neurons, output neurons]) # Shape of the network
+
+for s in range(samples):
+    qrnn.feedforward()
+    # Calculate Loss
+    qrnn.backpropagation()
+```
+Check the [examples](https://github.com/bilkosem/queueing-rnn/tree/master/examples) folder to see detailed use 🔎.
 ## References 📚
 
-[Gelenbe, Erol. (1989). Random Neural Networks with Negative and Positive Signals and Product Form Solution. Neural Computation - NECO. 1. 502-510. 10.1162/neco.1989.1.4.502.](https://www.researchgate.net/publication/239294946_Random_Neural_Networks_with_Negative_and_Positive_Signals_and_Product_Form_Solution) 
+[1] [Gelenbe, Erol. (1989). Random Neural Networks with Negative and Positive Signals and Product Form Solution. Neural Computation - NECO. 1. 502-510. 10.1162/neco.1989.1.4.502.](https://www.researchgate.net/publication/239294946_Random_Neural_Networks_with_Negative_and_Positive_Signals_and_Product_Form_Solution) 
 
-[Gelenbe, Erol. (1993). Learning in the Recurrent Random Neural Network. Neural Computation. 5. 154-164. 10.1162/neco.1993.5.1.154.](https://www.researchgate.net/publication/220499635_Learning_in_the_Recurrent_Random_Neural_Network)
+[2] [Gelenbe, Erol. (1993). Learning in the Recurrent Random Neural Network. Neural Computation. 5. 154-164. 10.1162/neco.1993.5.1.154.](https://www.researchgate.net/publication/220499635_Learning_in_the_Recurrent_Random_Neural_Network)
 
-[Basterrech, S., & Rubino, G. (2015). Random Neural Network Model for Supervised Learning Problems. Neural Network World, 25, 457-499.](https://www.semanticscholar.org/paper/Random-neural-network-model-for-supervised-learning-Basterrech-Rubino/b2ebb88e1d78c726aab274ec149d65e86999cbef)
+[3] [Basterrech, S., & Rubino, G. (2015). Random Neural Network Model for Supervised Learning Problems. Neural Network World, 25, 457-499.](https://www.semanticscholar.org/paper/Random-neural-network-model-for-supervised-learning-Basterrech-Rubino/b2ebb88e1d78c726aab274ec149d65e86999cbef)
 
-[Hossam Abdelbaki (2020). rnnsimv2.zip (https://www.mathworks.com/matlabcentral/fileexchange/91-rnnsimv2-zip), MATLAB Central File Exchange. Retrieved September 22, 2020.](https://www.mathworks.com/matlabcentral/fileexchange/91-rnnsimv2-zip?s_tid=FX_rc1_behav)
+[4] [Hossam Abdelbaki (2020). rnnsimv2.zip (https://www.mathworks.com/matlabcentral/fileexchange/91-rnnsimv2-zip), MATLAB Central File Exchange. Retrieved September 22, 2020.](https://www.mathworks.com/matlabcentral/fileexchange/91-rnnsimv2-zip?s_tid=FX_rc1_behav)
 
 
 ## License