Visual flowchart/flow diagram would be displayed here
Technical Implementation: ```python
class CNNParameterAnalyzer:
def init(self):

self.layer_types = {  
'convolutional': self.calculate_conv_params,  
'fully_connected': self.calculate_fc_params,  
'batch_norm': self.calculate_bn_params  

}

def calculate_conv_params(self, input_channels, output_channels, kernel_size):
    """Calculate parameters in a convolutional layer"""

Parameters = (kernel_width * kernel_height * input_channels * output_channels) + output_channels

    if isinstance(kernel_size, int):
        kernel_size = (kernel_size, kernel_size)

    weight_params = kernel_size[0] * kernel_size[1] * input_channels * output_channels
    bias_params = output_channels

    return weight_params + bias_params

def calculate_fc_params(self, input_size, output_size):
    """Calculate parameters in a fully connected layer"""
    return (input_size * output_size) + output_size

def calculate_bn_params(self, num_features):
    """Calculate parameters in a batch normalization layer"""

Scale and shift parameters

    return 2 * num_features

def analyze_typical_cnn(self):
    """Analyze a typical CNN architecture like ResNet-50"""

Simplified ResNet-50 structure

    layers = [

Initial conv layer

        ('conv', {'input_channels': 3, 'output_channels': 64, 'kernel_size': 7}),

Residual blocks (simplified)

        ('conv', {'input_channels': 64, 'output_channels': 64, 'kernel_size': 3}),
        ('conv', {'input_channels': 64, 'output_channels': 64, 'kernel_size': 3}),

        ('conv', {'input_channels': 64, 'output_channels': 128, 'kernel_size': 3}),
        ('conv', {'input_channels': 128, 'output_channels': 128, 'kernel_size': 3}),

        ('conv', {'input_channels': 128, 'output_channels': 256, 'kernel_size': 3}),
        ('conv', {'input_channels': 256, 'output_channels': 256, 'kernel_size': 3}),

        ('conv', {'input_channels': 256, 'output_channels': 512, 'kernel_size': 3}),
        ('conv', {'input_channels': 512, 'output_channels': 512, 'kernel_size': 3}),

Final classification layer

        ('fc', {'input_size': 512, 'output_size': 1000})
    ]

    total_params = 0
    layer_breakdown = []

    for i, (layer_type, config) in enumerat