An overview of the artificial neural networks in the computing architecture and science

That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow nets.

An overview of the artificial neural networks in the computing architecture and science

Introduction Artificial intelligence AIdeep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems.

For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms.

The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience.

That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

The primary motivation and driving force for these areas of study, and for developing these techniques further, is that the solutions required to solve certain problems are incredibly complicated, not well understood, nor easy to determine manually.

Increasingly, we rely on these techniques and machine learning to solve these problems for us, without requiring explicit programming instructions. This is critical for two reasons.

Second, even if we did know how to write the programs, they would be inordinately complex and nearly impossible to get right. Luckily for us, machine learning and AI algorithms, along with properly selected and prepared training data, are able to do this for us.

Artificial Intelligence Overview In order to define AI, we must first define the concept of intelligence in general. A paraphrased definition based on Wikipedia is: Intelligence can be generally described as the ability to perceive information, and retain it as knowledge to be applied towards adaptive behaviors within an environment or context.

While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals. So what does that mean exactly, when is it useful, and how does it work? In addition to speech recognition and natural language processing, generation, and understanding applications, AI is also used for other recognition tasks pattern, text, audio, image, video, facial, …autonomous vehicles, medical diagnoses, gaming, search engines, spam filtering, crime fighting, marketing, robotics, remote sensing, computer vision, transportation, music recognition, classification, and so on.

An overview of the artificial neural networks in the computing architecture and science

Something worth mentioning is a concept known as the AI effect. This despite the fact that these applications still fit the definition of AI regardless of widespread usage. There are many different goals of AI as mentioned, with different techniques used for each.

The primary topics of this article are artificial neural networks and an advanced version known as deep learning. Biological Neural Networks Overview The human brain is exceptionally complex and quite literally the most powerful computing machine known.

The inner-workings of the human brain are often modeled around the concept of neurons and the networks of neurons known as biological neural networks. At a very high level, neurons interact and communicate with one another through an interface consisting of axon terminals that are connected to dendrites across a gap synapse as shown here.

By LadyofHats [Public domain], via Wikimedia Commons In plain english, a single neuron will pass a message to another neuron across this interface if the sum of weighted input signals from one or more neurons summation into it is great enough exceeds a threshold to cause the message transmission.

This is called activation when the threshold is exceeded and the message is passed along to the next neuron. The summation process can be mathematically complex. In addition, each neuron applies a function or transformation to the weighted inputs, which means that the combined weighted input signal is transformed mathematically prior to evaluating if the activation threshold has been exceeded.

This combination of weighted input signals and the functions applied are typically either linear or nonlinear. These input signals can originate in many ways, with our senses being some of the most important, as well as ingestion of gases breathingliquids drinkingand solids eating for example.

A single neuron may receive hundreds of thousands of input signals at once that undergo the summation process to determine if the message gets passed along, and ultimately causes the brain to instruct actions, memory recollection, and so on.

This happens as a direct result of learning and experience.In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks. That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow nets.

But while the news from the last chapter is discouraging, we won't let it stop us. This overview aims to provide a self‐contained, uniform introduction to the different ways in which randomization can be applied to the design of . Deep learning in neural networks: An overview.

Author links open overlay panel Jürgen Schmidhuber. Show more. subfield of Deep Learning (DL) in Artificial Neural Networks (NNs). A standard neural network (NN) consists of many simple, connected processors called neurons, each producing a sequence of real-valued activations.

When a golf player is first learning to play golf, they usually spend most of their time developing a basic swing. Only gradually do they develop other shots, learning to chip, draw and fade the ball, building on and modifying their basic swing. Computational intelligence and soft computing.

Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the s. Artificial neural networks A neural According to one overview.

An overview of the artificial neural networks in the computing architecture and science

An artificial neural network uses the human brain as inspiration for creating a complex machine learning system. There are now neural networks that can classify millions of sounds, videos, and images.

Randomness in neural networks: an overview - WIREs Data Mining and Knowledge Discovery