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Modeling, Estimation and Gradient Descent
We will explore modeling through estimation and prediction. Along the way, we will get experience with an iterative method for guess-and-check called gradient descent. These approaches are helpful throughout data science particularly the field of machine learning.
Practice reasoning about a model and building intuition for loss functions.
Determining the gradient of a loss function with respect to model parameters and using the calculations for gradient descent.
We will apply these skills in Homework 6 to a real-world dataset.
Attachment:- Python.zip
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what are the issues assciated with top-down analysis please explain?
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