Private: AI Unleashed: Mastering AI at Your Pace

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Deep Neural Network (DNN)

Email Spam Filtering: Neural Network

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Real-Life Example: Email Spam Filtering

Scenario:

Imagine you receive dozens of emails every day, and you want to ensure that spam emails are automatically filtered out so you only see important messages in your inbox.

How a Neural Network Works:

  1. Input Layer:
    • The input layer of the neural network receives the email data. Each email is represented by a set of features, such as the presence of certain keywords, the email’s sender, the email’s subject line, and so on. For instance, “free,” “discount,” “click here,” etc., might be features that often appear in spam emails.
  2. Hidden Layers:
    • The neural network has one or more hidden layers where the actual computation happens. Each neuron in a hidden layer takes inputs from the previous layer, applies a weight to each input, sums them up, applies an activation function to the result, and passes the output to the next layer.
    • For example, if an email contains the word “free,” a neuron in the first hidden layer might recognize this feature and give it a high weight, signaling that the email might be spam.
  3. Output Layer:
    • The output layer provides the final classification. In this case, it might have two neurons: one for “spam” and one for “not spam.” The neuron with the higher value after processing the inputs indicates the network’s prediction.
    • If the network has learned that the combination of certain keywords and features is indicative of spam, it will activate the “spam” neuron more strongly.

Training the Neural Network:

  • Data Collection:
    • Initially, you collect a large set of emails, each labeled as either “spam” or “not spam.” These labeled examples form the training dataset.
  • Training Process:
    • The neural network is trained using this dataset. During training, the network adjusts the weights of the connections between neurons to minimize the error in its predictions. This is done using a process called backpropagation, where the network learns from its mistakes by comparing its predictions to the actual labels and adjusting accordingly.
    • Over time, the network learns to recognize patterns that are common in spam emails and those that are not.

Real-World Application:

  • Once trained, the neural network can be used to filter incoming emails in real-time. When a new email arrives, the network processes it through its layers and outputs a prediction on whether it is spam or not.
  • This helps keep your inbox clean and ensures that you only see important emails, saving you time and reducing the chances of missing important messages.

In summary, just like how your brain learns to identify patterns and make decisions based on experience, a neural network learns from data to recognize patterns and make predictions, such as distinguishing spam emails from non-spam emails.