def initialization(conf):
    """Initialize the parameters of the network.

    Args:
        layer_dimensions: A list of length L+1 with the number of nodes in each layer, including
                          the input layer, all hidden layers, and the output layer.
    Returns:
        params: A dictionary with initialized parameters for all parameters (weights and biases) in
                the network.
    """
    params = {}
    index = 1
    # Skip first element by using 1:
    for layerdim in conf['layer_dimensions'][1:]:
        variance = np.sqrt(2 / conf['layer_dimensions'][index - 1])
        # Mean is automatically set to 0, so I don't have to specify np.random.normal(mean, var, size)
        params['W_' + str(index)] = np.random.normal(scale=variance,
                                                     size=(conf['layer_dimensions'][index - 1], layerdim))
        params['b_' + str(index)] = np.zeros(layerdim)
        index += 1
    return params