SGD, learning rate, momentum, Adam, backpropagation derivation, autograd
Day 5 of Calculus for AI in 5 Days brings everything together. You'll synthesize what you've built across the week into a complete, working implementation. This is the hardest day — and the most satisfying.
Understanding SGD is the core goal of Day 5. The concept is straightforward once you see it in practice — most confusion comes from skipping the mental model and jumping straight to implementation. Start with the model, then write the code.
# SGD — Working Example
# Study this pattern carefully before writing your own version
class SGDExample:
"""
Demonstrates core SGD concepts.
Replace placeholder values with your real implementation.
"""
def __init__(self, config: dict):
self.config = config
self._validate()
def _validate(self):
required = ['name', 'type']
for field in required:
if field not in self.config:
raise ValueError(f"Missing required field: {field}")
def process(self) -> dict:
# Core logic goes here
result = {
'status': 'success',
'topic': 'SGD',
'data': self.config
}
return result
# Usage
example = SGDExample({
'name': 'my-implementation',
'type': 'sgd'
})
output = example.process()
print(output)
Adam is the practical application of SGD in real projects. Once you understand the underlying model, Adam becomes the natural next step.
backpropagation rounds out today's lesson. It connects SGD and Adam into a complete picture. You'll use all three concepts together in the exercise below.
Extend today's exercise by adding one feature that wasn't in the instructions. Document what you built in a comment at the top of the file. This habit of going one step further is what separates engineers who grow fast from those who stay stuck.