Answer / b.sunil kumar reddy

A neural network can be defined as a model of reasioning
based on the human brain.
The human brain incorporates nearly 10 billion neurons and
60 trillion connections,Synapses,between them.
By using multiple neurons simultaneously,the brain can
perform its functions much faster than the faster computers.

Although a single neuron has a very simple structure,an
army of such elements constitutes a tremendous processing
power.
The network which represents the connections among several
neurons is called a neural network.

Answer / john martin

An artificial neural network is a mathematical or
computational model for information processing based on a
connectionist approach to computation.
There is no precise agreed definition amongst researchers as
to what a neural network is, but most would agree that it
involves a network of relatively simple processing elements,
where the global behaviour is determined by the connections
between the processing elements and element parameters.
The original inspiration for the technique was from
examination of bioelectrical networks in the brain formed by
neurons and their synapses. In a neural network model,
simple nodes (or "neurons", or "units") are connected
together to form a network of nodes — hence the term "neural
network".

Answer / lipika priyadarshini bhoi

Neural network has the high computational rate than the
other conventional computer.
Neural network can perform tasks that a linear program can not.
When an element of the neural network fails,it can continue
without any problem by their parallel nature.
A neural network learns & does not need to be reprogrammed.

Answer / sun

The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in a nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
2. Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets.
This article focuses on the relationship between the two concepts; for detailed coverage of the two different concepts refer to the separate articles: biological neural network and artificial neural network

How artificial neurons learns?

0 Answers  

---

What can you do with an nn and what not?

0 Answers  

---

Explain Generative Adversarial Network.

0 Answers  

---

How are weights initialized in a network?

0 Answers  

---

How are artificial neural networks different from normal computers?

0 Answers  

---

What is a neural network and what are some advantages and disadvantages of such a network?

0 Answers  

---

What are neural networks? What are the types of neural networks?

0 Answers  

---

An auto-associative network is: a) a neural network that contains no loops b) a neural network that contains feedback c) a neural network that has only one loop d) a single layer feed-forward neural network with pre-processing

1 Answers  

---

What is the advantage of pooling layer in convolutional neural networks?

0 Answers  

---

 A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

1 Answers  

---

Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

1 Answers  

---

 Neuro software is: a) A software used to analyze neurons b) It is powerful and easy neural network c) Designed to aid experts in real world d) It is software used by Neuro surgeon

2 Answers   Bihar State Power Holding Company Limited BSPHCL,

---