Experimental Machine learning is turning out to be so much fun! After my investigations on replacing some signal processing algorithms with deep neural network, which for the interested reader has been documented in the article “ Machine Learning and Signal Processing”, I got around to trying the other two famous neural network architectures: LSTM and CNN.

Introducing CNN and LSTM

Before we get into the details of my comparison, here is an introduction to, or rather, my understanding of the other neural network architectures. We all understand deep neural network, which are simply a set of neurons per layer interconnected sequentially to another set of neurons in the next layer and so on. Each neuron implements the equation y = f(Wx_ + b)_ for inputs x and output y, where f is the non-linear activation function, W is the weight matrix and b is the bias. Here is a picture from  https://playground.tensorflow.org/

#neural-networks #lstm #machine-learning #cnn #keras