Seaborn is a popular data visualization library in Python that provides a heatmap function, commonly used to visualize correlation matrices. Heatmaps are effective for displaying the correlation between variables, making it easier to identify relationships in complex datasets.

L2 Regularization, also known as weight decay, penalizes the squared magnitude of the coefficients in deep learning models. It adds a term to the loss function that discourages large weight values, helping to prevent overfitting. By penalizing the magnitude of weights, L2 regularization encourages the model to distribute its learning across many features, resulting in smoother weight values and reducing the risk of overfitting.

Data Engineers play a pivotal role in designing and maintaining big data infrastructure, such as Hadoop or Spark clusters. They are responsible for ensuring that the infrastructure is efficient, scalable, and suitable for data processing and analysis needs.

The company should ethically address this concern by "Improving data anonymization and transparency." This approach allows the company to provide personalized recommendations while safeguarding customer privacy. By being transparent about data usage and ensuring that data is properly anonymized, the company can strike a balance between personalization and privacy.

Transfer Learning is the technique that involves leveraging a pre-trained model on a new, but related task. It allows you to take advantage of knowledge acquired from one domain and apply it to a different but related problem, which can save time and resources in training deep learning models.

In this case, a collaborative filtering approach is most appropriate. It recommends products based on the behavior and preferences of users who are similar to the target user. Content-based and item-based filtering consider product characteristics, while reinforcement learning is used for sequential decision-making.

Seasonal Decomposition of Time Series (STL) is a method for breaking down time series data into its seasonal, trend, and residual components. By analyzing the residuals, one can detect outliers that do not adhere to the typical temporal patterns in the data.

The Box-Cox transformation is used to adjust the distribution of data to be closer to a normal distribution. It does this by raising the data to a specific power, which is determined based on the data's characteristics. This can help with statistical modeling.

Vanilla RNNs often suffer from vanishing and exploding gradients, which hinder their ability to learn from and retain information over long sequences. Vanishing gradients make it challenging to train the network effectively. This is a key issue in recurrent neural networks.

To standardize data, you subtract the mean from the value and then divide by the standard deviation. In this case, the standardized value for a data point with a value of 11 is (11 - 5) / 2 = 3.5. (Option C)