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.

Long Short-Term Memory (LSTM) is a variant of RNN designed to handle long-term dependencies by maintaining a cell state that can capture information over long sequences. LSTM's ability to store and retrieve information over extended time steps makes it well-suited for tasks involving long-term dependencies in data sequences.

The F1 Score is a metric that balances the trade-off between precision and recall. It is especially useful when dealing with imbalanced datasets or when you want to find a balance between correctly identifying positive cases (precision) and capturing all positive cases (recall). The F1 Score is the harmonic mean of precision and recall. It is a suitable choice for evaluating models when both precision and recall are essential.

Transfer learning allows the startup to use pre-trained deep neural networks (e.g., a pre-trained CNN) as a starting point. This approach significantly reduces training time and computational resources, while still benefiting from the capabilities of deep learning.

In real-time data processing, Apache Spark is commonly used to ingest and process streaming data. Spark provides the capabilities to handle streaming data in real time, making it a popular choice for such applications.

The median is the most resistant measure of central tendency in a skewed distribution. It is less affected by extreme values or outliers since it represents the middle value when the data is arranged in order. The mean, mode, and geometric mean can be heavily influenced by outliers, causing them to be less representative of the data's central location.

Regularization is a common technique used to prevent overfitting in linear regression models. It adds a penalty term to the linear regression's cost function to discourage overly complex models. Regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.

Semi-structured data often contains a mix of structured tables and unstructured text. It's a flexible data format that can combine organized data elements with more free-form content, making it suitable for a wide range of data types and use cases, such as web data and NoSQL databases.

In transfer learning, a model trained on a large dataset is used as a starting point, and the knowledge gained is transferred to a new, similar task. This leverages the pre-trained model's knowledge to improve performance on the new task, particularly when the tasks are related.

When working with large datasets that do not fit into memory, the Python library "Dask" is a useful tool for efficient computations. Dask provides parallel and distributed computing capabilities, enabling data scientists to handle larger-than-memory datasets using familiar Python tools.