# Stock Market Prediction Using Multi-Layer Perceptrons With TensorFlow

This post is part of a series on artificial neural networks (ANN) in TensorFlow and Python.

In this post a multi-layer perceptron (MLP) class based on the TensorFlow library is discussed. The class is then applied to the problem of performing stock prediction given historical data. Note: This post is not meant to characterize how stock prediction is actually done; it is intended to demonstrate the TensorFlow library and MLPs.

A MLP network consists of layers of artificial neurons connected by weighted edges. Neurons are denoted $n_{ij}$ for the $j$-th neuron in the $i$-th layer of the MLP from left to right top to bottom. Inputs are fed into the leftmost layer and propagate through the network along weighted edges until reaching the final, or output, layer. An example of a MLP network can be seen below in Figure 1. Continue reading “Multi-Layer Perceptrons and Back-Propagation; a Derivation and Implementation in Python”