Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.
Thanks. Good intro. What’s missing? An example. Like pattern recognition or NLP text extraction.
Thanks for your suggestion, will add examples in my upcoming articles.
1 - Shouldn’t it be - 2/n in the first derivate you calculated?
2 - In the third derivate, the result should be sum(xi) and not just xi.
3 - Since the result of the third derivate is sum(xi), considering that the bias is when xi = 1, instead of multiplying for 1, you should multiply for n.