Study sources on Deep Learning

GRADIENT DESCENT METHODS
S. Ruder. An overview of gradient descent optimization algorithms.
https://arxiv.org/abs/1609.04747

INTRODUCTION TO DEEP LEARNING (CNN, RNN)
Yann LeCun, Yoshua Bengio & Geoffrey Hinton. Deep Learning.
https://www.nature.com/articles/nature14539

CNN
Vincent Dumoulin and Francesco Visin. A guide to convolution arithmetic for deep
learning. https://arxiv.org/pdf/1603.07285
https://www.v7labs.com/blog/convolutional-neural-networks-guide
https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
https://www.educative.io/answers/what-is-a-neural-network-flatten-layer
https://www.tensorflow.org/tutorials/images/cnn

RNN (LSTM, GRU)
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks

AUTOENCODERS
https://towardsdatascience.com/generating-images-with-autoencoders-77fd3a8dd368
https://www.v7labs.com/blog/autoencoders-guide
http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/
https://blog.keras.io/building-autoencoders-in-keras.html