Every Machine Learning Algorithm Can Be Represented as a Neural Network

It seems that all of the work in machine learning -- starting from early research in the 1950s -- cumulated with the creation of the neural network. Successively, algorithm after new algorithm were proposed, from logistic regression to support vector machines, but the neural network is, very literally, the algorithm of algorithms and the pinnacle of machine learning. It's a universal generalization of what machine learning is, instead of one attempt of doing it. In this sense, it is more of a framework and a concept than simply an algorithm, and this is evident given the massive amount of freedom in constructing neural networks -- hidden layer & node counts, activation functions, optimizers, loss functions, network types (convolutional, recurrent, etc.), and specialized layers (batch norm, dropout, etc.), to name a few. From this perspective of neural networks being a concept rather than a rigid algorithm comes a very interesting corollary: any machine learning algorithm, be it decision trees or k-nearest neighbors, can be represented using a neural network.