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1 vote

Gradient descent implementation of logistic regression

I think your implementation is correct and the answer provided is just wrong. Just for reference, the below figure represents the theory / math we are using here to implement Logistic Regression with ...
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1 vote

Understanding Conjugate Gradient Optimization methods

The important thing to understand is the fact that given an equation to solve $Ax=b$ and using the fact that $A$ is positive-definite and symmetric one can derive an inner product space from $A$ (one ...
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1 vote

Choosing Right Optimiser and Data Scaling

If you are working on a project that is similar to plenty of other ones, then you should apply what usually works the best, because such a project has been redone over and over again with different ...
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change parameterization to eliminate weight constraints in neural networks

For other people asking this question: In keras / tensorflow I observed way better results using custom constraints instead of a parameterization. When using a parameterization, the weights stayed ...
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1 vote

Need advice on possible DS techniques for a problem

Discovering the best prices is based on sales prediction: the more the demand will be, the higher you can increase the price and vice versa. But before making predictions through data science models, ...
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1 vote
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Optimize daily ice cream profit beased on simulation of all combinations input variables

I would propose a solution like this: Train a regression model which predicts the sales (target variable) based on all the features (both types: those you have control on and those you don't). ...
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Marketing Spend Optimization Techniques

I would try portfolio optimization, where you maximize something like the expected return while minimizing costs like risk. To do that, you'll need to assign an expected return (for example, the ROI) ...
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