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How Neural Networks Work

1. Forward Propagation:
   • Data is passed through the network from the input layer to the output layer. Each neuron computes a weighted sum of its inputs, applies an activation function, and passes the result to the next layer.
     
2. Activation Functions: Functions that introduce non-linearity into the model, allowing it to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, [attachment=811][attachment=811]and tanh.
   
3. Loss Function: Measures how well the network's predictions match the actual outcomes. Common loss functions include mean squared error for regression tasks and cross-entropy for classification tasks.
   
4. Backpropagation: A method used to update the weights and biases of the network based on the error calculated from the loss function. This process involves computing the gradient of the loss function and adjusting weights to minimize the error.
   Neural networks are used in a variety of applications, including:
   • Image and Video Recognition: Convolutional Neural Networks (CNNs) are particularly effective in this area.
     
   • Natural Language Processing: Recurrent Neural Networks (RNNs) and transformers are commonly used for tasks like language translation and sentiment analysis.
     
   • Speech Recognition: Neural networks can convert spoken language into text.
     
   • Game Playing: Used in reinforcement learning to enable agents to learn strategies in games.
     
   
   
   

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