Deep Learning is the most exciting and powerful branch of Machine Learning. It's a technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.
In this article we'll have a quick look at artificial neural networks in general, then we examine a single neuron, and finally (this is the coding part) we take the most basic version of an artificial neuron, the perceptron, and make it classify points on a plane. Have you ever wondered why there are tasks that are dead simple for any human but incredibly difficult for computers? Artificial neural networks (short: ANN's) were inspired by the central nervous system of humans. Like their biological counterpart, ANN's are built upon simple signal processing elements that are connected together into a large mesh. ANN's have been successfully applied to a number of problem domains: Agreed, this sounds a bit abstract, so let's look at some real-world applications.
Feedforward neural networks are also known as Multi-layered Network of Neurons (MLN). These networks of models are called feedforward because the information only travels forward in the neural network, through the input nodes then through the hidden layers (single or many layers) and finally through the output nodes. In MLN there are no feedback connections such that the output of the network is fed back into itself. These networks are represented by a combination of many simpler models(sigmoid neurons). Before we talk about the feedforward neural networks, let's understand what was the need for such neural networks.
An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this post, we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi-Layer Perceptron. ANNs are at the core of Deep Learning. They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., Google Images), powering speech recognition services (e.g., Apple's Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., Youtube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind's AlphaGo).