The sigmoid activation function maps any input to a value between 0 and 1. It's commonly used in neural networks for binary classification problems and helps introduce non-linearity in the network's computations.

Overfitting in neural networks can be controlled by reducing the capacity of the network, which refers to the number of nodes and layers. A simpler network is less likely to overfit as it has fewer parameters to learn and generalize more effectively.

In computer vision, detecting specific features or patterns in an image is often achieved using Convolutional Neural Networks (CNNs). CNNs are well-suited for image feature extraction and are widely used in tasks like object detection and image classification.

The blank should be filled with "Sigmoid." The Sigmoid activation function maps input values to the range of 0 to 1. It can cause the vanishing gradient problem, which makes training deep networks difficult due to its derivative approaching zero for extreme input values.

Data points that are far from their cluster centroids are likely outliers. Outliers can significantly impact clustering results. To address this issue, you can consider different strategies such as removing outliers, using robust clustering algorithms, or applying feature scaling and normalization to make the clusters less sensitive to outliers.

Model versioning is a crucial aspect of model deployment. It enables organizations to update machine learning models without causing downtime. This is vital in real-world applications where models need to adapt to changing data and conditions.

Data Science aims to collect and analyze data to gain insights and make data-driven decisions. While the other options are important aspects of Data Science, the primary goal is to gather and analyze data effectively.

RNNs (Recurrent Neural Networks) are known for their ability to retain memory from previous inputs in a sequence, making them effective for tasks like speech recognition, where the order of input data and contextual information is crucial for accurate prediction. Speech recognition relies on capturing temporal dependencies in audio data, which RNNs excel at.

Raw logs from web servers often contain unstructured data, as they can consist of a mix of text, images, and various file types that lack a specific format. Unstructured data is not organized in a traditional tabular structure.

Sentiment Analysis is an NLP task that involves determining the emotional tone or sentiment behind a series of words, often classifying it as positive, negative, or neutral. It's crucial for understanding public opinion and customer feedback.