Team Gen Aces

What is the use of He Initializer (HeNormal and HeUniform) ?

• Useful for maintaining the scale of the gradients.

• Often used to prevent extreme values.

• Useful for deep networks to prevent gradients from vanishing or exploding.

• Often used for layers with ReLU activations.

Answer explanation

He Initializer (HeNormal and HeUniform) is often used for layers with ReLU activations because it helps maintain the scale of the gradients by initializing weights in a way that accounts for the non-linearity introduced by ReLU. This prevents gradients from vanishing or exploding, facilitating more effective training of deep networks.

What is the primary purpose of an activation function in a neural network?

• To initialize the weights

• To normalize the input data

• To introduce non-linearity into the model

• To perform gradient descent

Answer explanation

The primary purpose of an activation function in a neural network is to introduce non-linearity into the model. This allows the network to learn and represent complex patterns in the data, which linear transformations alone cannot achieve.

Situation: You are implementing a neural network using ReLU activation functions. The model converges slowly, and you suspect it might be due to improper initialization of weights.

Which weight initializer is most appropriate in this case?

• Xavier (Glorot) Initialization

• He Initialization

• Zero Initialization

• Random Uniform Initialization

Answer explanation

ReLU (Rectified Linear Unit) activation functions are widely used in neural networks due to their ability to mitigate the vanishing gradient problem. However, they require proper initialization to work effectively. He Initialization is specifically designed for layers with ReLU activations. It initializes the weights using a random distribution with a mean of zero and a variance of \(2/n\), where \(n\) is the number of input units in the layer. This helps in maintaining the variance of the activations through the layers, thus facilitating faster convergence and avoiding issues like exploding or vanishing gradients.

Situation: You are working on developing a neural network model for a classification task. You have split your data into training and test datasets. After training your model, you evaluate its performance on the test dataset and achieve satisfactory results. However, when you deploy the model, it does not perform as well on new, unseen data.

Why is it important to also have a validation dataset in this scenario?

• The validation dataset helps in finalizing the model architecture and hyperparameters without overfitting to the test dataset.

• The validation dataset provides additional data for training, improving model performance.

• The validation dataset is used to train the model alongside the training dataset.

• The validation dataset ensures the model achieves 100% accuracy on the test dataset.

Answer explanation

The validation dataset is crucial because it allows you to tune your model's hyperparameters and make decisions about the model architecture during the training process. By evaluating the model on the validation set, you can get an unbiased estimate of its performance on unseen data. This helps in preventing overfitting to the training data and ensures that the model generalizes well. The test dataset should only be used for final evaluation, providing a measure of the model's performance after all tuning and training are complete. Using the test dataset for hyperparameter tuning can lead to overfitting to the test data, resulting in poor generalization to truly unseen data.

Situation: You are setting up a neural network for a classification task. You have imported your data and are about to feed it into the neural network.

What is the default datatype of the input features (X) and target labels (y) in neural networks, assuming you're using popular deep learning libraries like TensorFlow or PyTorch?

• dtype=float64

• dtype=int32

• dtype=float32

• dtype=object

Answer explanation

In popular deep learning libraries like TensorFlow and PyTorch, the default datatype for input features (X) and target labels (y) in neural networks is typically float32. This datatype is commonly used due to its efficiency in terms of memory consumption and computational performance, making it suitable for training large neural networks on GPU accelerators.

Situation: You are experimenting with three different architectures of artificial neural networks (ANNs) for a classification task. Each model is trained for the same number of epochs and with the same optimizer and loss function.

Based on the provided ANN code snippets, which model do you think will perform better on the given task?

• 
• 
• 

Answer explanation

Explanation: To determine which model is likely to perform better, we need to consider the complexity of the architectures. ANN Code 1 has two hidden layers with 64 units each, ANN Code 2 has two hidden layers with 128 units each, and ANN Code 3 has two hidden layers with 32 units each.

Intuitively, a higher number of units in the hidden layers (as in ANN Code 2) allows the model to learn more complex patterns in the data, potentially leading to better performance. However, too many units can also lead to overfitting if the dataset is not sufficiently large.

In this scenario, ANN Code 2 with 128 units in each hidden layer is likely to perform better than the other models due to its higher capacity to capture intricate patterns in the data.

To check the status and information about the Kubernetes cluster, you can use the command ______.

Scenario: A web developer wants to create a hyperlink to another webpage. Which HTML element should they use?

• <link>

• <a>

• <href>

• <hyper>

Situation: You want to apply a background color to your entire webpage. Which CSS property should you use?

• page-color: #f0f0f0;

• background: #f0f0f0;

• bg-color: #f0f0f0;

• body-background: #f0f0f0;

What is the result of the above JavaScript code?

• Hello World!

• <b>Hello</b> World!

• Hello World!

• None of the above