Gradient Descent is a widely used optimization algorithm in machine learning, particularly for training deep neural networks. It iteratively updates the model's parameters by moving in the direction of the negative gradient of the loss function.

Regularization techniques aim to reduce overfitting in machine learning models. Overfitting occurs when a model learns the training data too well and starts making predictions that are too specific to the training set, leading to poor performance on new data.

Cross-Entropy Loss is a widely used loss function for classification tasks in machine learning. It measures the difference between the predicted probability distribution and the true probability distribution of the class labels.

The learning rate controls the step size of parameter updates in optimization algorithms. It determines how much the model's parameters are adjusted in each iteration based on the gradient of the loss function.

L2 Regularization, also known as weight decay, adds a penalty term to the loss function that is proportional to the squared magnitude of the model's weights. This helps prevent overfitting by penalizing large weights and encouraging smaller, more generalized weights.

Hyperparameter tuning aims to optimize the performance of a machine learning model by finding the best combination of hyperparameters, such as the learning rate, regularization parameters, and model architecture. The goal is to maximize the model's accuracy, minimize loss, or achieve other desired performance metrics.

Simulated Annealing is an optimization algorithm that is designed to handle non-convex optimization problems. It uses a probabilistic approach to search for the global minimum of a function by gradually reducing the temperature parameter, which controls the probability of accepting worse solutions.

The momentum term in gradient-based optimization algorithms helps accelerate convergence by accumulating past gradients and using them to influence the direction of future updates. This can help overcome local minima and plateaus in the loss function, leading to faster convergence to the optimal solution.

Mean Squared Error (MSE) is a widely used loss function for regression tasks in machine learning. It measures the average squared difference between the predicted values and the true target values. Minimizing MSE helps the model learn to make accurate predictions for continuous variables.

Early stopping is a technique used to prevent overfitting in machine learning models. It involves monitoring the model's performance on a validation set during training and stopping the training process when the model starts to overfit the training data.

Simulated Annealing is an optimization algorithm that is designed to find the global minimum of a function. It uses a probabilistic approach to search for the global minimum by gradually reducing the temperature parameter, which controls the probability of accepting worse solutions. This helps the algorithm escape local minima and find the true global minimum.

Batch normalization is a technique used in deep neural networks to reduce internal covariate shift. Internal covariate shift occurs when the distribution of activations in a neural network changes during training, which can make the network more difficult to train. Batch normalization helps stabilize the distribution of activations by normalizing them across each batch of data.

Cross-Entropy Loss is a widely used loss function for multi-class classification tasks in machine learning. It measures the difference between the predicted probability distribution and the true probability distribution of the class labels. Minimizing Cross-Entropy Loss helps the model learn to make accurate predictions for multiple classes.

Dropout is a technique used in deep neural networks to prevent overfitting. It involves randomly dropping out some neurons during training, which helps reduce the network's reliance on individual neurons and encourages it to learn more generalizable features. Dropout helps improve the model's performance on unseen data.

Stochastic Gradient Descent (SGD) is an optimization algorithm that is designed to handle large-scale optimization problems. It uses a subset of the training data (a batch) to compute the gradient and update the model's parameters. SGD is widely used in deep learning due to its efficiency and ability to scale to large datasets.