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ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES SCHOOL OF INFORMATION SCIENCEBILINGUAL SCRIPT IDENTIFICATION FOR OPTICAL CHARACTER RECOGNITION OF AMHARIC AND ENGLISH PRINTED DOCUMENTSERTSE ABEBEJUNE,

How to fill out artificial neural network approach

How to fill out artificial neural network approach

1. To fill out the artificial neural network approach, follow these steps:
2. Identify the problem you want to solve using the neural network.
3. Gather and preprocess the data for training the neural network.
4. Determine the structure of the neural network, including the number of layers and neurons.
5. Initialize the weights and biases of the neural network.
6. Implement the forward propagation algorithm to compute the output of the neural network.
7. Define an appropriate cost function to measure the error between the predicted output and the desired output.
8. Use backpropagation algorithm to update the weights and biases of the neural network based on the error.
9. Repeat steps 5-7 for a number of iterations or until the desired accuracy is achieved.
10. Test the trained neural network on a separate set of data to evaluate its performance.
11. Fine-tune the neural network if necessary to improve its accuracy.
12. Deploy the trained neural network for making predictions or solving the problem.

Who needs artificial neural network approach?

1. Artificial neural network approach is needed by individuals or organizations who are dealing with complex problems that can benefit from the pattern recognition, learning, and decision-making capabilities of neural networks.
2. It is useful in various domains such as finance, healthcare, image and speech recognition, natural language processing, recommendation systems, robotics, and many others where recognizing patterns and making accurate predictions are crucial.
3. Researchers, data scientists, engineers, and analysts often utilize artificial neural network approaches to solve problems that are difficult for traditional algorithms.
4. Industries like banking, retail, marketing, and manufacturing can also benefit from using artificial neural network approaches to analyze large amounts of data and make informed decisions.

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What is artificial neural network approach?

Artificial neural network approach is a type of machine learning model inspired by the human brain's neural network.

Who is required to file artificial neural network approach?

Researchers, data scientists, and developers working in the field of machine learning are required to file artificial neural network approach.

How to fill out artificial neural network approach?

To fill out an artificial neural network approach, one must define the network architecture, specify the input data, choose the activation functions, and train the model using appropriate algorithms.

What is the purpose of artificial neural network approach?

The purpose of artificial neural network approach is to recognize patterns in data, make predictions, and solve complex problems that traditional algorithms may struggle with.

What information must be reported on artificial neural network approach?

The information reported on artificial neural network approach includes the model architecture, training data, validation results, and performance metrics.