As per Wikipedia, Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.
An ANN is a machine that models the way in which the brain performs a particular task. Haykin (1999) provides the following definition of an ANN: an artificial neural network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: (i) knowledge is acquired by the network from its environment through a learning process and (ii) interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge.
Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives. A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
Let us first see ANN architecture. There are mainly three layers in artificial neural networks.
1. Input Layer: The input layer is the one that contains neurons that are responsible for the feature inputs. In addition to the neurons for features, there is also a neuron for bias added to the input layer. So there are n+1 neurons in total in the input layer. Bias is responsible for the transfer of the line or curve from the origin.
2. Hidden Layer: The hidden layers are the layers that are between input and output layers. The number of hidden layers can be varied based on the application and need. Deep Neural Networks are the ones that contain more than one hidden layer.
3. Output Layer: The output layer contains neurons responsible for the output of a classification or prediction problem. The number of neurons in it is based on the number of output classes.
Therefore, the collection of parameters that define an ANN architecture are: number of layers, generally one input layer and one output layer and 0 or more intermediate (hidden); the number of neurons by layer, one or more; and the connectivity grade between the neurons, which is the number of connections between the neurons in different layers or between neurons in the same layer.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear. In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be “buy,” “hold” or “sell.” Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.
ANNs could be classified according to:
• The number of layers: ANNs can be Monolayer — one input layer and one output layer - or Multilayer, generalization of the last one, which are added hidden layers between the input and the output.
• The connection type: ANN can be Feedforward, if the signal propagation is produced in just one way, therefore, they do not have a memory, and Recurrent, if they keep feedback links between neurons in different layers, neurons in the same layer or in the same neuron.
• The connection grade: ANN can be Totally connected, in the case where all the neurons in a layer are connected with the neurons in the next layer (feedforward networks) or with the neurons in the last layer (recurrent networks), and Partially connected networks, in the case when there is not total connection between neurons from different layers.
• The learning paradigm, networks can be supervised or unsupervised (or hybrid).
Between the main neuronal models there are:
• Perceptron, is a supervised network, monolayer, feedforward and is the base for the most of the a architecture of the ANNs.
• Backpropagation, as the perceptron, the backpropagation network uses supervised learning, and it is multilayer. The importance of this network is its generalization capacity or produce satisfactory outputs for inputs that the system has never seen before during its training phase.
• Self-organized maps, they constitute a practice of unsupervised learning and competitive. They can be applied to cover two basic functionalities as classificatory or to represent multidimensional data in less dimension spaces (normally one or two dimensions), preserving the topology from the input.
Neural networks are broadly used, they are applied in financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection, risk assessment and many others. Neural Networks help to solve the problems without extensive programming with the problem-specific rules and conditions. They are simplified models used for many similar kinds of problems with most complex mathematical computation as behind the scenes. Neural networks are much faster in prediction after it has been trained than a conventional program.
Text classification is an essential part in many applications, such as web searching, information filtering, language identification, readability assessment, and sentiment analysis. Neural networks are used to convert handwritten characters into digital characters that a machine can recognize.
ANNs are playing a big role in image and character recognition. A CNN architecture makes it possible to predict objects and faces in images using industry benchmark datasets with up to 95% accuracy, greater than human capabilities which stand at 94% accuracy Given ANNs ability to take in a lot of inputs, process them to infer hidden as well as complex, non-linear relationships, They are the a machine learning algorithm that can take in an input image, assign importance to various aspects/objects in the image, and be able to differentiate one from the other We can optimize the size of our data using image compression neural networks. the neural network image recognition algorithm analyzes smaller parts of the images, for example, within the head, the cat’s nose, whiskers, ears, etc. The final output is a vector of probabilities, which predicts, for each feature in the image, how likely it is to belong to a class or category. It is the ideal application to save memory and optimize it. Image recognition is an ever-growing field with widespread applications from facial recognition in social media, cancer detention in medicine to satellite imagery processing for agricultural and defense usage.
Forecasting problems are complex, for example, predicting stock prices is a complex problem with a lot of underlying factors. Interest in using ANNs for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs.
The world is wide open for anybody jumping out there to start knowing what is a neural network and exploring the field. The more you understand the concepts, the better you can apply them in various fields. While the adoption of AI is growing with each passing day, companies across the world are facing the AI skills crisis. There is no better time than now to upskill yourself and become an Artificial Intelligence Engineer.