explain why activation functions are necessary in neural networks. discuss what would happen if activation functions were not used?

Activation functions in neural networks are crucial for several reasons:

1. Non-linearity: Real-world data is often non-linear. If we don't apply an activation function, the neural network would only be able to learn linear relationships. This is because without an activation function, a neural network is essentially just a linear regression model. The activation function does the non-linear transformation to the input making it capable to learn and perform more complex tasks.

2. Complex Patterns: Activation functions allow neural networks to learn from complex data, find patterns and make intelligent decisions. They help to decide whether a particular neuron should be activated or not based on the weighted sum of the inputs.

3. Gradient-based learning: Activation functions and their derivatives are used in the backpropagation process of a neural network, which helps in adjusting the weights and bias, and this is how the network learns during the training process.

If activation functions were not used in neural networks, the model would not be able to learn from the errors and make corrections. It would also not be able to handle non-linear data, which would limit its use to only simple tasks. The model would essentially become equivalent to a linear regression model, which is not capable of solving complex problems that neural networks are used for.