what are the different types of layer one can find in a neural network ?
In a neural network, there are generally three types of layers: input layers, hidden layers, and output layers.

 1   Input layers: This is the first layer of a neural network, where the input data is fed into the network. Each node in the input layer represents a feature or an attribute of the input data.

 2   Hidden layers: These are the layers between the input and output layers. Hidden layers can be further divided into several types, including:

  * Fully connected layers: Every node in this layer is connected to every node in the previous layer.
   * Convolutional layers: These are commonly used in image processing tasks. Each node in a convolutional layer receives inputs from a small region of the previous layer, and these regions are shifted across the layer to cover the entire input.
  *  Recurrent layers: These are used in sequential data processing tasks, such as natural language processing. Each node in a recurrent layer receives input not only from the previous layer but also from the previous time step.

 *   Output layers: This is the final layer of a neural network, which produces the output of the network. The number of nodes in the output layer is dependent on the type of problem being solved. For example, in a binary classification task, there will be one node in the output layer that represents the probability of belonging to one of the two classes. In a multi-class classification task, there will be multiple nodes in the output layer, each representing the probability of belonging to one of the classes.