# Here's how to build a multi-layered neural network in Python As businesses are recognizing the importance of neural networks, there is an increasing demand for the appropriate implementation of the same. Therefore, you can opt for a neural network tutorial. If you find the prospect of building and implementing neural networks to be exciting, then you can choose Imarticus Learning's AIML program.

How to Develop a Neural Network with Multiple Layers in Python

A master's in artificial intelligence is essential to have a career in neural networking. However, you can start with the basics like building a network with Python code. Take a look at the following steps to learn more.

Step 1: Prepare the Functions and Variables

To prepare all the functions and variables, you need to use the NumPy library. It is easier to do the calculations with this tool. Once your calculations are complete, you can move on to function activation. For this, you need to use the logistic sigmoid function.

Once you have all the values, you can decide on the learning rate, the input layer dimensionality, and the hidden layer dimensionality. This is crucial for a multi-layer neural network. You also need to determine the epoch count.

Next, you need to fill the weight matrices with the np. random.uniform() function. Keep in mind that the values will be between -1 and +1. When this is complete, you need to set the empty arrays which are necessary for the values of preactivation and post-activation which are found in the hidden layer.

Step 2: Import the Training Data

Use the Pandas library to import training data that is stored in Excel. Once you import it, you will need to convert that data to the NumPy matrix.

Step 3: Initiate Feedforward Processing

Feedforward is a part of the neural network that is present within the computations that lead to the output. In the first loop, you will find more than one epoch value. You can calculate the output from each epoch value. The third loop is where you will need to check each hidden node separately and use the dot product to get the preactivation value. This will help to generate the post-activation signal.

Finally, you can calculate the value for the output node. To do this, you need to calculate the pre-activation signals with the help of the dot product, and then use the activation function to get the post-activation signal. To know the final error, you will have to subtract the target from the post-activation signal of the output node.

Step 4: Back-Propagation of the Output

You need to reverse the direction once the feedforward processing is complete. You need to first shift from the output node to the hidden-to-output weights and then to the input-to-hidden weights. This back-propagation will help to provide the error data that you can use to train the neural network.

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