In Gradient Boosting, the learning rate (Option A) is adjusted at each step to minimize residual errors. A smaller learning rate makes the model learn more slowly and often leads to better generalization, reducing the risk of overfitting.

Gradient clipping is a technique used to mitigate the gradient explosion problem in deep learning. It limits the magnitude of gradients during training, preventing them from becoming too large and causing instability.

In image processing, adjusting the contrast or brightness of an image is termed as "Enhancement." Image enhancement techniques are used to improve the visual quality of an image by enhancing specific features such as brightness and contrast.

The primary challenge in real-time data processing, as opposed to batch processing, is latency. Real-time processing requires low-latency data handling, meaning that data must be processed and made available for analysis almost immediately after it's generated. This can be a significant challenge, especially when dealing with large volumes of data and ensuring near-instantaneous processing and analysis.

Data Ingestion is the process of extracting vast amounts of data from various sources and converting it into a format suitable for analysis. It is a crucial step in preparing data for analysis and reporting.

Median filtering is commonly used to reduce noise in an image. It replaces each pixel value with the median value in a local neighborhood, making it effective for removing salt-and-pepper noise and preserving the edges and features in the image.

Generative Adversarial Networks (GANs) are used to generate realistic digital content, such as images, videos, and even text. This trend leverages AI to create content that can be nearly indistinguishable from human-generated content, which has applications in various domains.

Python is commonly used in Data Science for data manipulation and analysis due to its extensive libraries like Pandas and ease of use. It provides a wide range of tools for working with data and is highly versatile for various data analysis tasks.

The primary cause of extremely small gradients in deep neural networks is vanishing gradients, often caused by the use of deep activation functions like sigmoid or tanh. As gradients propagate backward through many layers, they tend to approach zero, which can slow down training. Proper initialization techniques and activation functions like ReLU can help mitigate this issue.

The primary objective of feature scaling is to make features with different scales or units compatible so that machine learning algorithms, particularly those based on distance metrics, are not biased towards features with larger scales. This ensures that each feature contributes equally to the model's performance. Improving interpretability and visualization may be secondary benefits of feature scaling, but the main goal is compatibility.