This repository is the only place on the internet you will ever need for any reference material, research papers, books etc related to data science, mathematics, machine learning and deep learning.

I would highly recommend checking these websites out if you want to gain more knowledge on the topic of deep learning.

• https://jakevdp.github.io/PythonDataScienceHandbook/
• http://www.kareemalkaseer.com/books/ml/word-of-intro
• https://www.inferentialthinking.com/chapters/intro
• https://mathematical-tours.github.io/book-basics/
• http://neuralnetworksanddeeplearning.com/chap1.html
• https://iamtrask.github.io/2015/07/27/python-network-part2/
• https://stats.stackexchange.com/questions/154879/a-list-of-cost-functions-used-in-neural-networks-alongside-applications
• https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
• https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/
• https://peterroelants.github.io/posts/cross-entropy-softmax/
• https://arstechnica.com/gaming/2016/06/an-ai-wrote-this-movie-and-its-strangely-moving/
• http://colah.github.io/posts/2015-08-Understanding-LSTMs/
• https://medium.com/mlreview/understanding-lstm-and-its-diagrams-37e2f46f1714
• http://karpathy.github.io/2015/05/21/rnn-effectiveness/
• http://www.ai-junkie.com/ann/som/som1.html
• https://www.visualcinnamon.com/2013/07/self-organizing-maps-creating-hexagonal.html
• https://probablydance.com/2016/04/30/neural-networks-are-impressively-good-at-compression/
• https://blog.keras.io/building-autoencoders-in-keras.html
• http://mccormickml.com/2014/05/30/deep-learning-tutorial-sparse-autoencoder/

PS: I would highly appreciate help with the citations if needed, and would highly appreciate any thoughtful comments on how this repository can be improved.