1. What is an artificial neural network and for what types o…

1. What is an artificial neural network and for what types of problems can it be used? 2. Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar? 3. What are the most common ANN architectures? For what types of problems can they be used? 4. ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode. 5.Go to Google Scholar (scholar.google.com). Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain. Observe commonalities and differences among their findings and prepare a report to summarize your understanding. 6.Go to neuroshell.com. Look at Gee Whiz examples.Comment on the feasibility of achieving the results claimed by the developers of this neural network model.

1. An artificial neural network (ANN) is a computational model inspired by the structure and functions of biological neural networks in the brain. It is composed of interconnected artificial neurons (also called nodes or units) that process and transmit information. ANNs are used for solving complex problems in various domains such as image and speech recognition, natural language processing, pattern recognition, data classification, regression analysis, and control systems.

2. Biological and artificial neural networks have certain similarities and differences. One aspect that is not fully mimicked by artificial networks is the level of complexity exhibited by biological networks. Biological networks are highly parallel, with an astronomical number of interconnected neurons and intricate patterns of connectivity. Artificial neural networks, on the other hand, have a limited number of layers and neurons, and their connectivity patterns are much simpler.

Similarly, biological networks exhibit plasticity, which enables them to adapt to changing environments and learn from experience. This plasticity is not yet fully captured in artificial networks, although there have been attempts to incorporate some forms of learning and adaptation.

However, artificial networks share some similarities with biological ones. Both types of networks involve the transmission and processing of information through interconnected units. Both rely on the principles of activation and inhibition to propagate and modulate signals. Additionally, both types of networks can exhibit emergent properties, where complex behaviors arise from the interactions of simple components.

3. There are several common architectures used in ANNs, each suitable for different types of problems. Some of the most common architectures include feedforward networks, recurrent networks, convolutional networks, and self-organizing maps.

Feedforward networks are the simplest type and consist of layers of neurons connected in a directed acyclic graph. They are commonly used for tasks such as classification and regression.

Recurrent networks have feedback connections that allow for the propagation of signals in loops. This architecture is particularly suitable for tasks that involve sequential or time-dependent data, such as speech recognition or natural language processing.

Convolutional networks are specialized for processing grid-like data, such as images or audio signals. They employ convolutional layers and pooling operations to extract hierarchical representations of the input data.

Self-organizing maps are used for clustering and data visualization tasks. They possess a competitive learning mechanism that enables them to find underlying patterns and structure in the input data.

The choice of architecture depends on the specific problem at hand and the characteristics of the data being processed.