Evaluate the accuracy of an artificial intelligence system : Implement a DNN Based Multi-Class Classification with MXNet

The video tutorial covers the creation of a deep neural network with three hidden layers using the maxnet library. It explains how to prepare and split the fashion dataset into training and testing sets using the caret package. The tutorial details the conversion of training data into predictors and the construction of the neural network with specific layers and activation functions. It also discusses the training process, model evaluation, and the implementation of the model on testing data to make predictions, highlighting the model's accuracy and robustness.

Which libraries are used for building the deep neural network architecture?

• PyTorch and Scikit-learn

• MaxNet and Carrot

• Theano and Lasagne

• TensorFlow and Keras

What is the purpose of using the 'createDataPartition' function from the caret package?

• To split the data into training and testing sets

• To visualize the data

• To augment the data

• To normalize the data

Why is the training data converted into a matrix form?

• To enhance data security

• To improve data visualization

• To facilitate mathematical operations

• To reduce the data size

How many neurons are defined in the first hidden layer?

• 256

• 64

• 512

• 128

What activation function is used in the first hidden layer?

• Tanh

• Sigmoid

• ReLU

• Softmax

What is the purpose of the softmax function in the model?

• To convert logits to probabilities

• To normalize input data

• To initialize weights

• To reduce overfitting

How many iterations are set for training the model?

• 30

• 40

• 20

• 10

What is the significance of setting a seed in model training?

• To ensure reproducibility

• To increase accuracy

• To speed up training

• To reduce memory usage

What does the confusion matrix help to determine?

• Model's speed

• Model's accuracy

• Model's complexity

• Model's size

What indicates a robust model in terms of classification?

• Low training time

• Low number of misclassifications

• High number of misclassifications

• High training time