Hello,

the following sites may help you:

http://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/lecture11.pdf

http://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/practicals/practical6.pdf

http://deeplearning.net/tutorial/lstm.html

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Thank you Yuan for the suggestions. Oxford lectures were helpful. Some other links which helped me understand everything clearly were:

1) https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/

2) https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/

3) https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21

4) https://www.analyticsvidhya.com/blog/2017/03/introduction-to-gradient-descent-algorithm-along-its-variants/

5) https://stats.stackexchange.com/questions/47590/what-are-good-initial-weights-in-a-neural-network

6) https://medium.com/usf-msds/deep-learning-best-practices-1-weight-initialization-14e5c0295b94

7) https://medium.com/@aidangomez/let-s-do-this-f9b699de31d9