# Tag Info

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 ...
• 124
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 ...
• 2,081
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 ...
• 2,178

### 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 ...
• 101
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, ...
• 2,178
1 vote
Accepted

### 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). ...
• 21.8k