Seagate Interview Questions, Process, and Tips for Freshers

Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model.

• Hyperparameters are parameters that are set before the learning process begins.

• Hyperparameter tuning involves adjusting hyperparameters to optimize the model's performance.

• Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization.

Neural networks are trained using algorithms that adjust the weights and biases of the network based on the input data and desired output.

• Neural networks are trained using a process called backpropagation, where the error between the predicted output and the actual output is used to adjust the weights and biases of the network.

• Training data is fed into the neural network, and the network's output is compared to the des...read more

Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.

• Overfitting happens when a model is too complex and captures noise in the training data.

• It can be identified when a model performs well on training data but poorly on unseen data.

• Techniques to prevent overfitting include cross-validation, regularization, and early ...read more