Basic Introduction of Neural Networks

What is Neural Networks?

A neural network is a type of machine learning that models by itself after the human brain. This creates an artificial neural network that through algorithm allows the computer to learn by integrating new data. We can simply understand the neural system as an artificial neural system composed of artificial neurons or nodes. It is a network or circuit of neurons or nodes.

Neural networks are one of the most beautiful programming models ever invented. Usually, we give commands to the computer, such as, taking inputs, breaking big problems into smaller problems, solving and processing them and at last giving the output as a result. But in a neural network, we don’t tell the computer how to solve our problem. Instead, it learns from observational data and examples, figuring out its solution to the same problem as hand.

How Does Neural Network Work?

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. A neural network contains layers of interconnected nodes.

In the era of technology, there are plenty of artificial intelligence algorithms, the neural networks can perform what has been termed as deep learning. The basic unit of the nervous system and the brain are the neurons, the most important and critical building block of an artificial neural network is a perception that can perform simple signal analyzing and processing, and these are then connected into a large tracery network.

The neural network computer is trained to perform a job by analyzing training examples earlier marked in advance. A popular instance of a task for a neural network using profound learning is a task of object recognition where a big number of objects of a certain sort, such as a cat or a street sign, are displayed to the neural network and the machine analyzes the recurring patterns in the presented images, learns to categorize new images.

The Learning Process of Neural Network

A neural network learns through many processes and mediums. We can simply say that it obtained information from different observations and different examples. It uses different algorithms like other programming models. Unlike other algorithms, it is not possible to program neural networks straight for the assignment with their deep learning. Rather, they need to know the data just like a child’s growing brain. The learning strategies are based on three methods.

Methods of Learning Strategies of Neural Networks:

1. Supervised Learning

This learning approach is the easiest because there is a marked dataset that the machine passes through and the algorithm is altered until it can process the dataset to obtain the required outcome.

2. Unmonitored Learning

This approach is used in instances where no marked dataset is accessible to learn from. The neural network analyzes the dataset, and then a cost function tells the neural network how far away it was from the target. Then the neural network adapts to improve the algorithm’s precision.

3. Reinforced Learning

The neural network is strengthened in this algorithm for favorable outcomes and punished for adverse outcomes, forcing the neural network to learn over time.

Different Types of Neural Networks

There are several kinds of Neural network implements on the basis of mathematical operations and a set of parameters that are required to determine the output. All these different types of neural networks use different principles in determining their own rules. There are many types of neural networks, each with their unique strengths. Here are some of the most important types of neural networks and their short description.

1. Feedforward Neural Network – Artificial Neuron

This is one of the simplest types of artificial neural networks. In a feedforward neural network, the data passes through the different input nodes until it reaches the output node.

2. Radial Basis Function Neural Network

A radial basis function considers the distance of any point relative to the center. Such neural networks have two layers. In the inner layer, the features are combined with the radial basis function.

3. Multilayer Perceptron

A multilayer perceptron has three or more layers. It is used to classify data that cannot be separated linearly. It is a type of artificial neural network that is fully connected. This is because every single node in a layer is connected to each node in the following layer.

4. Convolutional Neural Network

A convolutional neural network (CNN) uses a variation of the multilayer perceptrons. A CNN contains one or more than one convolutional layers. These layers can either be completely interconnected or pooled.

5. Recurrent Neural Network(RNN) – Long Short Term Memory

A Recurrent Neural Network is a type of artificial neural network in which the output of a particular layer is saved and fed back to the input. This helps predict the outcome of the layer.

6. Modular Neural Network

A modular neural network has a number of different networks that function independently and perform sub-tasks. The different networks do not really interact with or signal each other during the computation process. They work independently towards achieving the output.

7. Sequence-To-Sequence Models

A sequence to sequence model consists of two recurrent neural networks. There is an encoder that processes the input and a decoder that processes the output. The encoder and decoder can either use the same or different parameters. This model is particularly applicable in those cases where the length of the input data is not the same as the length of the output data.

History of Neural Network

While neural networks certainly represent powerful modern computer technology, the idea dates back to 1943, with two researchers at Chicago University, a neurophysiologist Warren McCullough and a mathematician Walter Pitts.

In 1943, Neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work. In 1949, Donald Hebb, a famous Canadian psychologist, wrote “The Organization of Behaviour”, a work which pointed out the fact that neural pathways are strengthened each time they are used (Hebbian Learning).

Their paper, “A Logical Calculus of Immanent Ideas in Nervous Activity,” was first published in Brain Theory, a journal that popularized the theory that activating a neuron is the basic unit of brain activity. However, this paper at the time had more to do with the development of cognitive theories, and in 1952 the two researchers moved to MIT to start the first department of cognitive science.

Neural networks in the 1950s were a fertile area for research on computer neural networks, including the Perceptron that performed visual pattern recognition based on a fly’s compound eye. Two researchers at Stanford University developed MADALINE (Multiple Adaptive Linear Elements) in 1959, with a neural network going beyond the theoretical and addressing an actual problem. MADALINE has been specifically used to decrease the amount of echo on a telephone line, to improve voice quality, and has been so successful that it remains in commercial use to the present time.

A noteworthy book from MIT in 1969, Perceptron: ‘An Introduction to Computational Geometry‘ tempered this, despite initial enthusiasm in artificial neural networks. The authors expressed their skepticism about artificial neural networks, and how in the quest for true artificial intelligence this was likely a dead end. This dulled this area for research significantly throughout the 1970s, both in the general interest as well as funding. Nevertheless, some efforts continued and the first multi-layered network was developed in 1975, paving the way for further development in neural networks, an achievement some thought impossible less than a decade earlier.

When John Hopfield, a professor at Princeton University, invented the associative neural network in 1982, interest was significantly renewed in neural networks; the innovation was that data could travel bidirectionally as it was previously only unidirectional, and is also known for its inventor as a Hopfield Network. Going forward, there has been wide popularity and growth in artificial neural networks.

Basic Applications of Neural Network

Neural technology has quite a wide range of applications and can be used in different fields. In the world of technology, few important technologies have been used which play an important role in cure and control health-related issues there have been different methods and applications of technology. Neural technology is one of the modern technologies to do so. Therefore, there has been lots of applications with the use of the neural network.

Some of the Application areas of Neural Networks Includes:

  • System identification and control (vehicle control, process control, natural resources management)
  • Quantum chemistry, game-playing and decision making (backgammon, chess, poker)
  • Pattern recognition (radar systems, face identification, object recognition and more)
  • Sequence recognition (gesture, speech, handwritten text recognition)
  • Medical diagnosis (it has been used to diagnose several cancers)
  • Financial applications (automated trading systems)
  • Data mining (or knowledge discovery in databases, “KDD”)
  • Visualization and e-mail spam filtering

Moreover, the applications and scope of artificial neural networks are quite vast, it can be used in almost any field to optimize efficiency and growth. Thus, its applications could only be limited up to our own imagination.

More about applications of Neural Networks

In Conclusion

In the modern era of computers and programming world, neural technology is found to be a revolution and can help us in different ways. It helps us in the diagnosis of different health issues and different diseases. By different means of visualization and examples, the computer helps us to understand the patterns and helps to categories them.

Neural networks can be said as the collection of programming algorithms that work to solve different neural problems. Having so many applications and advantages, this master collection of programming algorithms and data can easily change the life of many people.