NEURAL NETWORKS

As the name suggests, neural networks involve a relationship between networks and nervous systems. This relationship is modelled on how the human brain functions.?

A neural network is either a biological neural network, made up of real biological neurons, or artificial neuron network, that is designed to recognize patterns and solve business problems.

This has many modern applications such as driverless cars, personalized recommendations, object classification and detection, image tagging, language translation, and many more.??

There are three subfields of artificial intelligence that help AI in equipping high-performance computers having the powers and abilities that have the ability to match and exceed human abilities.?

In this blog, we will discuss the second subfield of AI, neural networks.

What Are Neural Networks?

A neural network is a set of algorithms that ventures to recognize basal relationships in a set of data in a way that mimics the human brain

Neural networks can easily adapt to the changing inputs; thus giving the best possible result without redesigning the output criteria. The neural network has its roots in artificial intelligence, and is gaining a lot of popularity in the development of trading systems.

• Neural networks are a series of algorithms that have the ability to mimic the human brain that helps in recognizing the vast amounts of data.
• The connections of neurons and synapses found in the brain are resembled by neural networks.

Understanding Neural Networks

The working of a neural network is similar to a human brain’s neural network. A “neuron” in a neural network is defined as a mathematical function that collects and classifies information on the basis of a specific structure Neural network strongly resembles the statistical methods such as curve fitting and regression analysis.

Neural network consists of many layers of interconnected nodes. Each node is termed as perceptron and it is the same as multiple layer regression. The signal is fed by a perceptron produced by a multiple layer regression into a nonlinear activation function.

In the world of finance, neural networks assist in the development of processes such as time-forecasting, algorithmic trading, securities classification, credit risk modelling, and constructing priority indicators and price derivatives.

Neural Networks: How They Mimic The brain

Artificial neural networks have the tendency to mimic the biological neural network in the human brain.

There are millions of innovations inspired by nature – everything from birds inspiring airplanes, burdocks inspiring Velcro, to whales inspiring wind turbines to go faster. In a similar manner,? the human brain has inspired the creation of neural networks.?

A human brain consists of approximately 100 billion miniscule cells called neurons. Each neuron is made up of a cell body which has many connections coming out of it: numerous dendrites (the cell's inputs – carrying information toward the cell body) and a single axon (the cell’s output – carrying information away from it).

From the neuron's cell body, dendrites extend and receive messages from other neurons. When the messages are received or sent by the neurons, there is a transmission of electrical impulses along the axons that aid in carrying out functions such as controlling muscles, storing memories, and many more.

There are some areas where the simplified model of the artificial neural networks does not quite mimic the brain in an exact manner. For example, artificial neural networks can't mimic the creation or destruction of connections (dendrites or axons) between neurons, and it simply ignores the signal timing.?

Despite this, these networks are quite effective considering how they're used in many applications, and have become smarter over time.

Importance Of Neural Networks

The ability to identify patterns, solve intricate puzzles, and adjust to changing surroundings of neural networks is quite essential. They have the capacity to learn from data and this has far-reaching effects, ranging from revolutionizing technology like natural language processing and self-driving automobiles to automating decision-making processes and increasing efficiency in numerous industries. This development of artificial intelligence drives innovation and influences the direction of technology, and is largely dependent on neural networks.

Neural networks have the ability to help computers make intelligent decisions with limited or no human assistance. The reason is that they can learn and model the relationships between input and output data that are complex and non-linear.?

What Are Neural Networks Used for?

There are several use cases of neural networks across many industries, such as:

• Medical diagnosis with the help of medical image classification
• Targeted marketing with the help of social network filtering and behavioral data analysis
• Financial predictions with the help of processing historical data of financial instruments
• Energy demand and electricity load forecasting?
• Process and quality control
• Identification of chemical compound

Here are the four most important applications of neural networks below:

Computer vision

The ability to extract information and insights from images and videos is termed as computer vision. Computers are able to distinguish and recognize images similar to humans with the help of neural networks. There are various applications of computer vision, such as:

• Visual recognition in self-driving cars
• ?Content moderation
• Facial recognition
• Image labelling

Speech recognition

Neural networks have the ability to analyze human speech despite being different speech patterns, tone, pitch, language, and accent. Virtual assistants like Alexa, Siri and automation transcription software use speech recognition to do tasks such as:

• Automatically classify calls
• Convert clinical conversations into documentation
• Accurately subtitle videos and meeting recordings

Natural language processing

The ability to process natural, human-created text is termed as natural language processing (NLP). Computers can gather insights and meaning from text data and documents with the help of neural networks.? There are several use cases of NLP, such as:

• Automated virtual agents and chatbots
• Automatic classification and organization of data
• Business intelligence analysis of long-term documents
• Indexing key phrases
• Document summarization
• Article generation

Recommendation Engines?

User activity can be tracked by neural networks for developing personalized recommendations. Neural networks can analyze all user behavior and discover new products and services that interest a particular user. Based on the search and the browsing history of the user, neural networks can recommend similar products to the user.

How Do Neural Networks Work?

The inspiration behind the architecture of neural networks is the human brain. Neurons, human brain cells, form a complex network and send electrical signals to each other that helps humans in processing information. A number of artificial neurons together made up the neural network that work together to solve a problem. Artificial neurons, called nodes, are software modules, and artificial neural networks, called neural simply neural networks, are software algorithms that, at their core, use computing systems to solve mathematical calculations.

Neural networks are complex systems that have the ability to mimic some features of the functioning of the human brain. It is made up of an input layer, one or more hidden layers, and an output layer, these layers are made up of layers of artificial neurons that are coupled to each other. The two stages of the basic processes are called back propagation and forward propagation.

Simple Neural Network Architecture

A basic neural network is made up of interconnected artificial neurons in three layers:

Input Layer

Information from the outside world enters through the input layer in the neural network. The function of the input layer is to process the data, analyze and categorize it, and pass it to the next layer.

Hidden Layer

The input of the hidden layer comes from the input layer or the other hidden layers. These artificial neural networks can have a very large number of hidden layers. Each hidden layer is responsible for analyzing the output of the previous layer, processing it further, and passing it on to the next layer.

Output Layer?

The output layer is responsible for giving the final result of all the data processing done by an artificial neural network. This layer can have single or multiple nodes. Suppose if we have a binary classification problem, then the output layer will have only one output node providing the result in the form of 0 or 1. But, if there is a multi-class classification problem, the output layer may have one or more output nodes.

Deep Neural Network Architecture

Deep neural networks, also known as deep learning networks, have many hidden layers with millions of artificial neurons linked together. A number, that is called weight, shows the connections between one node and another. The weight may be a positive or a negative number, positive if the one node excites another, and negative if the one node suppresses another. Modes that have higher weight value than the other nodes, have more influence on other nodes.

Theoretically, it is considered that deep neural networks can map any input type to any output type. Deep neural networks need more training as compared to any other machine learning methods. Deep neural networks need millions of examples of training data as compared to only hundreds or thousands of examples needed by a simpler network.

Working

Let us think of each node as its own linear regression model that consists of input data, weights, a bias (or threshold), and an output. The formula for this will be like this:

∑wixi +bias=w1x1+w2x2+w3x3+bias

output=f(x)=1 if ∑w1x1+b>=0; 0 if ∑w1x1+b<0

Weights are being assigned after the input layer is determined. These weights are helpful in determining the importance of any given variable, with larger weights contributing more to the output as compared to other inputs. After this, the inputs are multiplied by their respective weights and then summed. Then, the output is passed through an activation function, determining the output. If the output is greater than the given threshold, it “fires” (activates) the node, passing data to the next layer in the network. This results in the output of one node becoming the input to the next node in the network. This passing of data from one layer to the next layer makes this neural network a feedforward neural network.

Types Of Neural Networks

Artificial neural networks are classified on the basis of the flow of data from the input node to the output node. The neural networks are of the following types:

1. Feedforward Neural Networks

Feedforward neural network is a simple artificial neural network architecture as in this the data is processed in a single direction, from the input node to the output node. It has input, hidden, and output layers, and there are no feedback loops. In this type of neural network, every node in one layer is connected to every node in the next layer. This network uses a feedback process that helps in improving the predictions over time. It has a straightforward architecture that makes it appropriate for a number of applications, such as regression and pattern recognition.

1. Backpropagation algorithm

Correlative feedback loops are used continuously by artificial neural networks to improve their predictive analysis. In simple words, a neural network in which the data flows from input node to the output node through many different paths is correlative feedback. But there is only one path that is the correct one that maps the input node to the output node. For finding this path, a feedback loop is used by the neural network.

1. Multilayer Perceptron (MLP)?

MLP is a type of feedforward neural network that has three or more layers, one input layer, one or more hidden layers, and an output layer. Nonlinear activation functions are used by multilayer perceptron.

1. Convolutional Neural Networks (CNN)

A CNN is a specialized artificial neural network that is designed and used for image processing because they can extract relevant features from images that are useful for image recognition and classification. Several mathematical functions, like summarizing or filtering, called convolutions, are performed by the hidden layers in the neural networks. CNN employs convolutional layers so that it can automatically learn hierarchical features from input images, enabling effective image recognition and classification. Each hidden layer is responsible for extraction and processing of different image features, such as edges, color, and depth.

1. Recurrent Neural Network (RNN)

Recurrent Neural Network (RNN) is an artificial neural network that is intended for sequential data processing. Situations contextual dependencies are critical, such as time series prediction and natural language processing, RNN has application, since it makes use of feedback loops, which enable information to survive within the network.

1. Long Short-Term Memory (LSTM)

LSTM is a type of RNN which has the benefit of vanishing gradient problem in training RNNs. Memory cells and gates are used by LSTM to selectively read, write, and erase information.

Learning Of A Neural Network

There are three types of learnings of a artificial neural networks, as follows:

1. Learning With Supervised Learning

The neural network is being guided by a teacher who has access to both input and output pairs in supervised learning. The outputs are created by the inputs without surroundings being taken into. An error is generated based on the comparison of these outputs with the teacher-known desired outputs. To reduce the errors, the network’s parameters are changed iteratively and stop when the performance reaches an accurate level.

1. Learning With Unsupervised Learning

In unsupervised learning, equivalent output variables are not present. The main goal in this learning is to comprehend data’s (X) underlying structure. There is no instructor present to offer advice. The intended outcomes are the modelling data patterns and relationships. Regression and classification are the words that are related to supervised learning, whereas clustering and association are related to unsupervised learning.

1. Learning With Reinforcement Learning?

The network gains knowledge with the help of interaction between the environment and feedback in the form of rewards or penalties. The goal of the network is to find a strategy that optimizes cumulative rewards over time. This type of learning is frequently used in gaming and decision-making applicators.

Advantages Of Neural Networks

Neural networks are widely used as they have many benefits, as follows:

• Adaptability: Neural networks have the ability to adapt to new situations and learn from data, as they are useful for activities where the link is complex or not well defined between inputs and outputs.
• Pattern Recognition: Neural networks are proficient in recognizing patterns that renders them efficacious in tasks like audio and image identification, natural language processing, and other intricate data patterns.
• Parallel Processing: The neural networks can process numerous jobs at once, which speeds and improves the efficiency of computations, as they are capable of parallel processing by nature.
• Non-Linearity: Neural networks are the networks that have the ability to model and comprehend complicated functions found in data by the virtue of non-linear activation functions that are present in neurons, thereby overcoming the drawbacks of the linear models.
• Fault Tolerance: Artificial neural networks (ANNs) have fault tolerance ability, so the generation of the output is not affected by the corruption or fault in one or more cells.
• Observation Based Decisions: ANNs have the ability to learn from the events and thus make decisions based on those observations.
• Unorganized Data Processing: Neural networks have the ability to organize large amounts of data by processing, sorting, and categorizing it in an exceptionally good manner.
• Ability To Learn Hidden Relationships: Even without commanding any fixed relationship, neural networks can learn hidden relationships in data. Thus making neural networks better model highly volatile data and non-constant variance.
• Ability To Generalize Data: Neural networks can predict the output of unseen data as they have the ability to generalize and infer unseen relationships on unseen data.?

Disadvantages Of Neural Networks

Along with numerous benefits, there are some drawbacks of neural networks, including the following:

• Computational Intensity: Artificial neural network training process can be laborious and computationally demanding work, demanding a lot of computer power.
• Black Box Nature:? Neural networks can pose problems in important decisions as it is difficult to understand the method by which they make decisions.
• Overfitting: It is a process in which neural networks commit training material to memory rather than identifying patterns in the data provided. Even regularization approaches help to alleviate this, but the problem still exists.
• Need For Large Datasets: Neural networks need sizable, labelled datasets for efficient training; otherwise, performance of neural networks will suffer from incomplete or skewed data.
• Inaccurate Results: If the training of neural networks is not done properly, ANNs can generate incomplete or inaccurate results.
• ?Numerical Translation: Neural networks work with numerical information, so before presented to the ANN, data or problems must be translated into numerical values.

The Bottom Line

Artificial neural networks are complex, integrated systems that can perform analytics much deeper, faster, complete, and accurate than human capability. There are many types of neural networks that are suited for different problems or purposes and target outputs. In the finance sector, neural networks are widely used to analyze the transaction history, understand asset movement, and predict market outcomes.

In this article, we’ve understood that neural networks are systems or hardware that is designed to mimic the way human neurons work.

Don’t worry about neural networks taking the word; having them can change the world for the better sounds like a more positive and likely outcome for all!