But what *is* a Neural Network? - THE MATH YOU SHOULD KNOW!
Channel: CodeEmporium
Date: 2018-04-15

We'll take a look at how exactly neural networks learn by starting with modeling an objective function through Maximum Likelihood Estimation. We then take a look at neural network training using back propagation and Stochastic gradient descent.

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REFERENCES
[1] Neural Netorks and deep learning: http://neuralnetworksanddeeplearning.com/chap2.html
[2] Christopher Bishop
[3] Machine Learning Basics from the deep Learning book: http://www.deeplearningbook.org/contents/ml.html
[4] The principal of Maximum Likelihood: http://suriyadeepan.github.io/2017-01-22-mle-linear-regression/
[5] Stanford Deep Learning Handout: https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/pdfs/41%20DeepLearning.pdf