What can neural networks be used for?

Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

What is a neural network how can it be used as a tool for market analysis explain with an example?

“For example, a caller who is speaking with a dental office may ask to ‘schedule an appointment. ‘ The neural network will seek, find and classify that phrase as a conversation, therefore providing marketers with valuable insights into the performance of marketing initiatives.”

How can neural networks improve performance?

Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:

1. Increase hidden Layers.
2. Change Activation function.
3. Change Activation function in Output layer.
4. Increase number of neurons.
5. Weight initialization.
6. More data.
7. Normalizing/Scaling data.

What is a neural network give an example of a neural network?

Neural networks are designed to work just like the human brain does. In the case of recognizing handwriting or facial recognition, the brain very quickly makes some decisions. For example, in the case of facial recognition, the brain might start with “It is female or male?

What is neural network how neural network can be work and what are the advantage and disadvantages of neural network?

The network problem does not immediately corrode. Ability to train machine: Artificial neural networks learn events and make decisions by commenting on similar events. Parallel processing ability: Artificial neural networks have numerical strength that can perform more than one job at the same time.

Which neural networks has a memory?

This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

How can artificial neural networks improve decision making give example?

The structure of ANNs is commonly known as a multilayered perceptron, ie, a network of many neurons. In each layer, every artificial neuron has its own weighted inputs, transfer function, and one output. Once the ANN is trained and tested with the right weights decided, it can be given to predict the output.

What is performance of neural network?

Neural networks allow us the flexibility to define a topology, from number of neurons to number of hidden layers. Many have said that designing the topology is an art rather than a science.