Understanding what a neural network is can often be a very difficult process. This article is going to define what a neural network is and how they are built. We are also going to explore some of the latest breakthroughs that relate to them.
What Is a Neural Network?
A neural network is a cluster of information processing units that are designed to work in tandem to emulate the way biological nervous systems process information. These networks contain a hidden layer between what is known as the input and output layers.
If there are multiple hidden layers, then the processing unit is known as a deep neural network. These types of networks are generally capable of processing much more complex algorithms than their shallow network counterparts.
A deep neural network takes the given input information and passes it to the first network of hidden layers based on special rules known as an activation function. The information is then sent to the next hidden layer for further processing until it completes its journey to the output layer in the form of an answer.
The majority of these types of networks use a type of learning algorithm. These algorithms alter the connections that the network makes based on the type of input information that it is presented with. This helps the network learn by example using a wide variety of information types.
The most common class of networks is known as backpropagational neural networks. These networks use the delta rule to effectively learn based on past experiences. Machines using these rules typically make a guess as to the correct answer for a given problem. Depending on how far the answer was from the correct one, the weighted connections between the hidden layers are then adjusted to learn from the process This allows the network to learn from prior problems they are presented with.
How To Make A Neural Network
To make an artificial neural network, you will have to begin with a series of input layers. These layers will be used to store information for the network to process. The next still will be to design the hidden layer portion.
You can utilize a single hidden layer known as shallow learning or multiple hidden layers for deep learning. The next still will be to program the weighted connection variables that determine the path input information will take in the hidden layers while being processed.
You will also need to select and code a type of learning algorithm like the delta rule. This way the neural network can effectively learn from prior problems that it is presented with. The final step will be to create your output layer where the processed information is stored as an answer.
Once the setup is complete, the network is ready to begin processing information. As the network completes processing tasks, it is common to edit the weighted connection variables to improve the information processing rate of the network.
Examples Of Neural Networks
AI With Relationship Reasoning
The human mind is able to make sense of its surroundings and learn from it in a process known as relationship reasoning. For example, this learning type would allow you to tell that your coffee cup is to the upper left of your bowl of cereal.
Researchers at DeepMind are finally able to replicate this sense of spatial awareness with neural networks. To accomplish this, their module was trained by analyzing several different 3D shapes in varying colors and sizes.
After training, the system was tasked with answering various questions about an object in relation to the other shapes in an area. The visual answering tasks algorithm is known as CLEVR. The results using the relationship network were very impressive.
Using a typical visual question and answering architecture, the rate of answered questions was around 68.5 percent compared to 92.5 percent for humans. When they used the relationship network the success rate shot up to 95.5 percent.
While the technology is still definitely in its infancy, this has the capability of giving networks a sense of relationship reasoning that is capable of surpassing humans.
Quantum Entanglement Analysis
The interaction of quantum particles in a process known as quantum entanglement is a difficult process to understand for even the most talented physicist. With quantum entanglement, systems are entirely dependent on the status of all the other objects in that given system. One change can have a cascading effect on all the other elements.
Getting these types of states recorded on paper and tracked for research purposes can be a very difficult and time-consuming process. However, using a limited neural network is making the process much easier. Scientists recently created a network that utilizes two groups of hidden layers to make these complex complications. The first layer is known as a visible neuron and the second is a hidden neuron.
Using this processing method, these systems were able to simplify quantum problems for physicist by removing unwanted variables from the equation.
Neural Network Art
Using a type of network known as generative adversarial, a neural network is capable of creating artistic images of human faces that are surprisingly lifelike and detailed. Generative adversarial networks use two different networks. In this example, the first network works to create the image. The second network then functions to evaluate the images lifelike appearance before completing the final processing.
Some of the portraits speak for themselves. While the technology is still definitely a work in progress, this type of networking method may eventually pave the way for machine generated artwork.
The future of neural network development looks promising indeed. They are helping us to understand complex quantum entanglement theories, creating beautiful portraits of people, and even demonstrating a sense of relationship reasoning that is superior to that of humans. As the field continues to develop, we are sure to see even more exciting applications.