Constant learning in neural networks

Neural networks are digital replicas of the human brain. Learning in neural networks is one of the most important features as they need not be programmed and can learn from experience.

Neural networks and its types have a completely different approach to problem solving, as compared with conventional computers. While the latter follows a set of instructions to solve a problem that humans already know how to solve, constant learning in neural networks enables them to solve problems that humans can’t.

Why use neural networks?

Neural networks can be used to extract meaning, patterns, and trends from relatively complicated data, that are too complex to be noticed by either humans or computer techniques. Trained neural networks can be used to provide projections, when they encounter new situations, as they adapt themselves to it. They can generate representations of the data they receive during learning and can organize themselves accordingly. Besides, they can perform several computations simultaneously and can perform well even if there is a network damage.

What are the types of learning in neural networks?

Learning in neural networks can be classified into two major categories:

• Supervised learning incorporates an external teacher that tells each output unit what its response should be to input signals. Global information may be required during the learning process. The paradigms of supervised learning include error-correction learning, reinforcement learning, and stochastic learning. One of the most important issues concerning supervised learning is minimization of error between the desired and the computed values. Usually, supervised learning is performed off-line.
• Unsupervised learning, such as competitive learning and Hebbian learning, does not incorporate any external teacher and is dependent upon local information. It is referred to as self-organization as it self-organizes data to detect their emergent collective properties.

What are examples of neural networks?

Suppose there is a network to recognize handwritten digits. We may use around 256 sensors, each recording the presence or absence of ink in a small area of a single digit. Now, for each sensor, there will be 256 input units, 10 output units, and some hidden units as well. Whenever a sensor records a digit, the network should produce high activity in the correct output unit and low activity in other output units.

For training the network, an image of a digit is presented and the actual activity of 10 output units is compared with the desired activity. Then the error is calculated, which is defined as the square of the difference between the actual and the desired activities. After this, the weight of each transmission is changed so as to reduce error. This process is repeated for many different images of each type of digit, until the network categorizes each image correctly.

What are the applications of neural networks?

Neural networks find applications in various industries, such as healthcare, targeted marketing, risk management, and sales forecasting. For instance, in the field of healthcare, diagnosis is carried out by constructing the model of an individual’s cardiovascular system and comparing it with the physiological measurement of the patient in real time. This can help detect potentially harmful conditions at a nascent stage that can help fight diseases in an easier way. In the Airline industry, the Airline Marketing Tactician (AMT) consists of various brilliant technologies.

A feedforward neural network and AMT are coalesced to suggest booking advice for each departure. This kind of information increases the profitability of the airline company and provides a technological advantage to the people using the system.

The fact that neural networks learn from examples and can program themselves makes them flexible and powerful at the same time. Due to their various advantages over computer algorithms, scientists predict that someday neural networks will integrate with computing and AI, and will produce conscious networks.-