7 Open Source Libraries for Deep Learning Graphs - KDnuggets

If you're a deep learning enthusiast you're probably already familiar with some of the basic mathematical primitives that have been driving the impressive capabilities of what we call deep neural networks. Although we like to think of a basic artificial neural network as some nodes with some weighted connections, it's more efficient computationally to think of neural networks as matrix multiplication all the way down. We might draw a cartoon of an artificial neural network like the figure below, with information traveling in from left to right from inputs to outputs (ignoring recurrent networks for now). This type of neural network is a feed-forward multilayer perceptron (MLP). If we want a computer to compute the forward pass for this model, it's going to use a string of matrix multiplies and some sort of non-linearity (here represented by the Greek letter sigma) in the hidden layer: MLPs are well-suited for data that can be naturally shaped as 1D vectors.