Neural Network and Industry use case’s of Neural Network.
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
In more practical terms neural networks are non-linear statistical data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
An artificial neural network involves a network of simple processing elements (artificial neurons) which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters. Artificial neurons were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician, who first collaborated at the University of Chicago.
One classical type of artificial neural network is the recurrent Hopfield network.
The concept of a neural network appears to have first been proposed by Alan Turing in his 1948 paper Intelligent Machinery in which he called them “B-type unorganised machines”.
The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations and also to use it. Unsupervised neural networks can also be used to learn representations of the input that capture the salient characteristics of the input distribution, e.g., see the Boltzmann machine (1983), and more recently, deep learning algorithms, which can implicitly learn the distribution function of the observed data. Learning in neural networks is particularly useful in applications where the complexity of the data or task makes the design of such functions by hand impractical.
Neural networks can be used in different fields. The tasks to which artificial neural networks are applied tend to fall within the following broad categories:
- Function approximation, or regression analysis, including time series prediction and modeling.
- Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
- Data processing, including filtering, clustering, blind signal separation and compression.
Application areas of ANNs include nonlinear system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining (or knowledge discovery in databases, “KDD”), visualization and e-mail spam filtering. For example, it is possible to create a semantic profile of user’s interests emerging from pictures trained for object recognition.
✨ Artificial neural networks are a form of deep learning.
✨ They are also one of the main tools used in machine learning.
✨ Consequently ANN’s play an increasingly important role in the development of artificial intelligence.
✨ The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.
✨ This technique allows the system’s hidden layers to become versatile.
✨ Adapting to situations where the outcome doesn’t match the one originally intended.
✨ The development of deep learning neural networks has also helped in the development of Artificial Neural Networks.
✨ Deep learning neural networks are networks made up of multiple layers.
✨ This allows the system to become more versatile.
Different layers are able to analyse and extract different features.
This process allows the system to identify new data or images.
Theoretical and computational neuroscience is the field concerned with the analysis and computational modeling of biological neural systems. Since neural systems are intimately related to cognitive processes and behaviour, the field is closely related to cognitive and behavioural modeling.
The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (biological neural network models) and theory (statistical learning theory and information theory).
How do Artificial Neural Networks Work?
As we have seen Artificial Neural Networks are made up of a number of different layers, each layer houses artificial neurons called units these artificial neurons allow the layers to process, categorize, and sort information alongside the layers are processing nodes each node has its own specific piece of knowledge this knowledge includes the rules that the system was originally programmed with it also includes any rules the system has learned for itself.
This makeup allows the network to learn and react to both structured and unstructured information and data sets almost all artificial neural networks are fully connected throughout these layers.
Each connection is weighted the heavier the weight, or the higher the number, the greater the influence that the unit has on another unit the first layer is the input layer this takes on the information in various forms.
This information then progresses through the hidden layers where it is analysed and processed by processing data in this way, the network learns more and more about the information. eventually, the data reaches the end of the network, the output layer.
Here the network works out how to respond to the input data this response is based on the information it has learned throughout the process here the processing nodes allow the information to be presented in a useful way.
Industry Use cases of Neural Networks:
Artificial Neural Networks can be used in a number of ways.
They can classify information, cluster data, or predict outcomes ANN’s can be used for a range of tasks, these include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.
There are many types of Artificial Neural Network each has its own specific use depending on the task it is required to process the ANN can be simple or very complex.
The most basic type of Artificial Neural Network is a feedforward neural network this is a basic system where information can travel in only one direction, from input to output.
Top deep neural networks Companies:
Krisp.AI uses deep neural networks to bring a whole new level to voice audio quality. With Their AI technology users can mute all sorts of noises in their background that stop users from taking that conference call literally from anywhere.
Element AI is a global developer of AI software that helps people work smarter. We turn cutting-edge research and industry expertise into software solutions that exponentially learn and improve. Our end-to-end offering, including advisory services,…
Speechmatics converts speech to text thus making audio searchable and analysable. Speechmatics provides the world’s most accurate and cheapest speech recognition.
Microblink is a R&D company with a mission to simplify data entry in mobile and web apps using camera input. Using advanced AI methods, we develop state-of-the-art computer vision and data extraction technology, linking modern mobile apps with the…
Industrial Pharmacy Lab:
The Industrial Pharmacy Lab (IPL) was founded in 2001 and has been awarded the NETS Award and NETS Special Award of the Gebert Ruf Foundation, Basel in 2004. The IPL is a turning platform between the university and the pharmaceutical industry at the…