There are many other tutorials; I just seem to/or have worked well for me. I have numbered them in order of increased difficulty in getting started (not necessary difficult of work, good tutorials manage this within the tutorial):

1. Cloud Academy search for (AWS, or Machine Learning in the cloud)
2. https://aws.amazon.com/training/self-paced-labs/
3. Qwiklabs a. https://amazon.qwiklabs.com/catalog?locale=en b. https://amazon.qwiklabs.com/quests/20?locale=en
4. Code from Hans-on Labs (https://aws.amazon.com/serverless-workshops/) a. https://github.com/aws-samples/aws-serverless-workshops/tree/master/WebApplication b. https://github.com/aws-samples/aws-lambda-zombie-workshop

https://blog.csdn.net/changyuanchn/article/details/15681853

https://github.com/snatch59/keras-autoencoders

https://blog.keras.io/building-autoencoders-in-keras.html

https://github.com/keras-team/keras/tree/master/examples

https://www.kaggle.com/apapiu/manifold-learning-and-autoencoders

https://www.kaggle.com/rvislaywade/visualizing-mnist-using-a-variational-autoencoder

https://medium.com/@curiousily/credit-card-fraud-detection-using-autoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd

https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test

• https://towardsdatascience.com/neural-network-optimization-algorithms-1a44c282f61d

• https://arxiv.org/abs/1609.04747

• http://cs231n.github.io/neural-networks-3/

• https://theberkeleyview.wordpress.com/2015/11/19/berkeleyview-for-adam-a-method-for-stochastic-optimization/

• https://www.coursera.org/lecture/deep-neural-network/adam-optimization-algorithm-w9VCZ

• http://ruder.io/optimizing-gradient-descent/