Explains model parameters, tokens, and scaling laws in plain language. Learn how size, data quality, and training choices affect cost and accuracy—and how to pick the right model for a task.
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🔧 What Are AI Model Parameters? — Conceptual Process — Part 3
ptual Process
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Technical Implementation:
### Visual Architecture Overview
*Interactive visual representation would be displayed here*
For Implementation Details:
### Conceptual Process
*Visual flowchart/flow diagram would be displayed here*
Technical Implementation:
### Visual Architecture Overview
*Interactive visual representation would be displayed here*
For Implementation Details:
### Conceptual Process
*Visual flowchart/flow diagram would be displayed here*
Technical Implementation:
### Visual Architecture Overview
*Interactive visual representation would be displayed here*
For Implementation Details:
### Conceptual Process
*Visual flowchart/flow diagram would be displayed here*
Technical Implementation:
### Visual Architecture Overview
*Interactive visual representation would be displayed here*
For Implementation Details:
### Conceptual Process
*Visual flowchart/flow diagram would be displayed here*
Technical Implementation: ```python
class SimpleNeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# Calculate total parameters
# Layer 1: input_size * hidden_size weights + hidden_size biases
self.layer1_params = (input_size * hidden_size) + hidden_size
# Layer 2: hidden_size * output_size weights + output_size biases
self.layer2_params = (hidden_size * output_size) + output_size
self.total_parameters = self.layer1_params + self.layer2_params
def show_parameter_breakdown(self):
print(f"Network Architecture: {self.input_size} -> {self.hidden_size} -> {self.output_size}")
print(f"Layer 1 parameters: {self.layer1_params}")
print(f"Layer 2 parameters: {self.layer2_params}")
print(f"Total parameters: {self.total_parameters}")
# Example: Small image classifier
# Input: 784 pixels (28x28 image), Hidden: 128 neurons, Output: 10 classes
small_network = SimpleNeuralNetwork(784, 128, 10)
small_network.show_parameter_breakdown()
# Output:
# Network Architecture: 784 -> 128 -> 10
# Layer 1 parameters: 100,480 (784*128 + 128)
# Layer 2 parameters: 1,290 (128*10 + 10)
# Total parameters: 101,770