External links

• Andrew Ng's deep learning course: https://www.youtube.com/playlist?list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0
• Hidden Markov model: https://www.youtube.com/watch?v=TPRoLreU9lA
• Introduction to Hidden Markov Models by Degirmenci: https://scholar.harvard.edu/files/adegirmenci/files/hmm_adegirmenci_2014.pdf
• Issues and Limitations of HMM in Speech Processing: A Survey: https://pdfs.semanticscholar.org/8463/dfee2b46fa813069029149e8e80cec95659f.pdf
•  Words embeddings: https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/ and https://towardsdatascience.com/word-embeddings-exploration-explanation-and-exploitation-with-code-in-python-5dac99d5d795
• Understanding activation functions: https://ai.stackexchange.com/questions/5493/what-is-the-purpose-of-an-activation-function-in-neural-networks
• Improving the way neural networks work by Michael Nielson: http://neuralnetworksanddeeplearning.com/chap3.html#introducing_the_cross-entropy_cost_function
• Lecture 8 from the course, Natural Language Processing with Deep Learning by Stanford University: https://www.youtube.com/watch?v=Keqep_PKrY8
• On the difficulty of training recurrent neural networks: http://proceedings.mlr.press/v28/pascanu13.pdf