Optimization Considerations

Question 1

What does optimization focus on?

Answer 1

iteratively improving model performance by adjusting parameters and hyperparameters to minimize a loss function or maximize a desired objective, leading to more accurate and efficient models

Question 2

What is hyperparameter tuning?

Answer 2

Adjusting parameters that control the learning process (learning rate, number of layers or trees, regularization strength)

Question 3

What is feature selection

Answer 3

Identifying the most relevant features to improve model performance and reduce complexity

Question 4

What is model selection

Answer 4

Choosing the appropriate machine learning algorithm for your problem and data

Question 5

What is regularization

Answer 5

Techniques to prevent overfitting

Question 6

What are ensemble methods?

Answer 6

Combining multiple models to improve performance and robustness

Question 7

What are the Optimization Algorithms

Answer 7

Gradient Descent
Stochastic Gradient Descent
Adam
Bayesian Optimization

Question 8

What is gradient descent?

Answer 8

A fundamental algorithm for minimizing loss functions by iteratively adjusting parameters in the direction of the negative gradient

Question 9

What is Stochastic Gradient Descent?

Answer 9

A variation of gradient descent that uses small batches of data for faster training

Question 10

What is Adam?

Answer 10

An adaptive optimization algorithm that combines momentum and RMSprop for efficient training

Question 11

What is Bayesian Optimization?

Answer 11

A probabilistic approach to finding optimal hyperparameters by building a model of the objective function