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Understanding Neural Network What is a neural network? Figure 1: What is a Neural Network? The neural network is a system that mimics a human brain structure. Figure 1 presents a multi-layer feedforward Neural Network structure, which contains an input layer (green), hidden layers (pink) and an output layer (orange). Each layer contains multiple nodes indicated by a small circle, for example, input nodes are in green, hidden nodes are in pink, and output nodes are in orange. Nodes in different layers can connect to each other, and the connection strength between two nodes is called a weight between these nodes. The connection flow is started from the input layer to hidden layers, and then to the output layer. Each node in a hidden layer can accept input connections from a previously hidden layer or input layer. These input connections will be summed and fed to a special function called activation function to form its node's output. As a result, only hidden la

1. Introduction: In this article, we'll create a simple neural network with the  deeplearning4j  (dl4j) library – a modern and powerful tool for machine learning. Before we get started, not that this guide doesn't require a profound knowledge of linear algebra, statistics, machine learning theory and lots of other topics necessary for a well-grounded ML engineer. 2. What Is Deep Learning ? Neural networks are computational models that consist of interconnected layers of nodes. Nodes are neuron-like processors of numeric data. They take data from their inputs, apply some weights and functions to these data and send the results to outputs. Such network can be trained with some examples of the source data. Training essentially is saving some numeric state (weights) in the nodes which later affects the computation. Training examples may contain data items with features and certain known classes of these items (for instance, “this set of 16×16 pixels contains a hand-