ReLU (Rectified Linear Unit) is commonly used in hidden layers due to its simplicity and effectiveness.

ReLU (Rectified Linear Unit) is a widely used activation function that helps mitigate the vanishing gradient problem.

Logistic Regression is commonly used for classification tasks as it predicts the probability of a binary outcome.

Linear Regression is a common algorithm used for regression tasks, predicting a continuous output.

Queues are commonly used in breadth-first search algorithms to manage the nodes to be explored.

Stacks are commonly used for undo functionality in text editors, as they allow the last action to be reversed easily.

Arrays are often used to manage a playlist of songs because they allow for easy access and manipulation of the song list.

AVL trees are commonly used to maintain a sorted list of items due to their efficient search, insertion, and deletion operations.

Binary search is commonly used to find an element in a sorted array.

DFS is commonly used for topological sorting in directed acyclic graphs, as it can explore all vertices and edges in a depth-first manner.

Finding the shortest path in a graph is a common application of dynamic programming.

Finding the shortest path in a graph, such as using Dijkstra's algorithm, is a common application of dynamic programming.

Job scheduling is a common application of greedy algorithms, particularly in maximizing resource utilization.

Linear regression is commonly used for predicting continuous outcomes, such as stock prices based on historical data.

Linked lists are commonly used to implement stacks due to their dynamic size and ease of adding/removing elements.

Image recognition is a well-known application of neural networks, showcasing their ability to learn from visual data.

Neural networks are commonly applied in weather forecasting to analyze and predict weather patterns based on historical data.

Neural networks are widely used in image recognition tasks due to their ability to learn complex patterns in visual data.

Queues are commonly used in breadth-first search (BFS) algorithms for traversing graphs.

Regression analysis is commonly used to predict continuous outcomes, such as house prices based on various features.

Reinforcement learning is commonly applied in game playing, where an agent learns to make decisions through trial and error.

RNNs are commonly used for time series prediction due to their ability to process sequential data.

Stacks are commonly used to evaluate postfix expressions due to their LIFO nature.

Spam detection is a common application of supervised learning, where the model is trained to classify emails as spam or not.

Both top-down with memoization and bottom-up tabulation are common approaches to implement dynamic programming.

One common assumption of linear regression is that the residuals (errors) are normally distributed.

Linear regression assumes that there is no multicollinearity among the predictors, meaning they should not be highly correlated.

One common assumption in linear regression is that the residuals (errors) are normally distributed.

Integration with existing systems is a common challenge during model deployment, as it requires ensuring that the model can work seamlessly with other software and data pipelines.

All of the above are common challenges faced in MLOps, including data privacy regulations, lack of data, and overfitting models.