Gradient Descent is a widely used optimization algorithm for minimizing the loss function in neural networks.

All of the above are common programming languages used in web development.

Dynamic programming is often used in resource allocation problems where optimal distribution of limited resources is required.

Principal Component Analysis (PCA) is commonly used for feature selection by reducing dimensionality while retaining variance.

Mean imputation is a common technique where missing values are replaced with the mean of the available data.

Principal Component Analysis (PCA) is used to reduce dimensionality and select important features.

Using dropout layers is a common technique to prevent overfitting by randomly setting a fraction of input units to 0 during training.

Inlining functions is a common technique used in code optimization to reduce function call overhead.

Principal Component Analysis (PCA) is a dimensionality reduction technique that can also be used for feature selection by identifying the most important features.

Finite state machines are commonly used in lexical analysis to recognize tokens based on regular expressions.

TensorFlow Serving is a popular tool specifically designed for deploying machine learning models in production environments.

Decision Trees are commonly used for customer segmentation due to their ability to handle categorical data effectively.

Random Forests can be applied to various tasks, including image recognition, time series forecasting, and spam detection.

Churn prediction is a common application of supervised learning in marketing, helping businesses identify customers likely to leave.

TCP (Transmission Control Protocol) is a connection-oriented protocol that ensures reliable data transmission.

Decision Trees are prone to overfitting, especially when they are deep and complex.

DFS can get stuck in deep paths, leading to inefficient traversal in certain graphs.

All of the listed options are disadvantages of K-means clustering, making it sensitive to outliers, requiring prior knowledge of the number of clusters, and potentially converging to local minima.

A key disadvantage of K-means is that it requires the user to specify the number of clusters beforehand, which may not always be known.

Linked lists do not allow random access to elements, unlike arrays which provide direct access via indices.

Arrays have a fixed size, which can be a disadvantage when the number of elements is not known in advance.

Decision trees can easily overfit the training data, especially if they are not pruned or if the tree is too deep.

Linked lists have a memory overhead due to storing pointers, which makes them less memory efficient compared to arrays.

SVM is sensitive to the choice of kernel, which can significantly affect its performance and requires careful tuning.

Using too many features can lead to higher computational costs and may increase the risk of overfitting.

HTTP/2 introduces multiplexing, allowing multiple requests and responses to be sent simultaneously over a single connection.

AVL trees provide faster search times compared to Red-Black trees due to stricter balancing.

Binary search is more efficient than linear search, especially for large datasets, due to its O(log n) time complexity.

LR parsing can handle left recursion, which LL parsing cannot.

Random Forests reduce the risk of overfitting by averaging the predictions of multiple decision trees, leading to better generalization.