Adaface Sample Deep Learning Questions

We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with?
            The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. 
            
            Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend?

A: Perceptron
            B: SVM
            C: Logistic regression
            D: Guassion discriminant analysis

You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows:

The model is trained using the following parameters:
            
            - Batch size: 64
            - Learning rate: 0.001
            - Optimizer: Adam
            - Loss function: Categorical cross-entropy
            
            After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance?

A: Increase the batch size to 128
            B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer
            C: Replace Adam optimizer with SGD (Stochastic Gradient Descent)
            D: Decrease the number of filters in each Conv2D layer by half
            E: Increase the learning rate to 0.01
            F: Reduce the size of the Dense layer to 64 units

You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations:
            
            **Initial Setup:**
            
            - CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers.
            - Activation function: ReLU
            - No class-weighting or data augmentation.
            - Results: High overall accuracy, but poor precision and recall for minority classes.
            
            **Experiment 1:**
            
            - Changes: Implement class-weighting to penalize mistakes on minority classes more heavily.
            - Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased.
            
            **Experiment 2:**
            
            - Changes: Add dropout layers with a rate of 0.5 after each convolutional layer.
            - Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes.
            
            Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy?

A: Increase the dropout rate to 0.7
            B: Further fine-tune class-weighting parameters
            C: Increase the number of filters in the convolutional layers
            D: Add batch normalization layers after each convolutional layer
            E: Use a different activation function like LeakyReLU
            F: Implement more aggressive data augmentation on the minority class