Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN PyTorch CIFAR10 Examp

The video tutorial demonstrates how to implement a deep neural network using PyTorch on the CIFAR-10 dataset. It covers importing necessary packages, downloading and exploring the dataset, transforming data into tensors, and batching it. The tutorial then defines a neural network model, sets up a training routine with an optimizer and scheduler, and finally runs validation and testing to evaluate accuracy. The tutorial emphasizes the importance of using appropriate schedulers and optimizers for efficient training.

What is the primary purpose of importing the 'transforms' module from Torch vision?

• To handle data augmentation and preprocessing

• To train the model

• To evaluate the model

• To visualize the dataset

How many classes does the CIFAR-10 dataset contain?

• 5

• 20

• 15

• 10

What is the shape of an image in the CIFAR-10 dataset?

• 128x128x3

• 64x64x3

• 32x32x3

• 28x28x1

Which PyTorch class is used to create batches of data?

• DataLoader

• DataBatcher

• BatchLoader

• BatchProcessor

What activation function is used after the first linear layer in the model?

• Softmax

• ReLU

• Tanh

• Sigmoid

What is the purpose of using dropout in the model?

• To reduce the model's size

• To prevent overfitting

• To increase the model's complexity

• To speed up training

Which optimizer is used in the training routine?

• Adam

• Adagrad

• SGD

• RMSprop

What is the role of a learning rate scheduler?

• To adjust the learning rate during training

• To keep the learning rate constant

• To increase the learning rate over time

• To decrease the learning rate to zero

What is the main metric used to evaluate the model's performance on the validation set?

• F1 Score

• Accuracy

• Recall

• Precision

Which type of neural network is suggested as better for image data?

• Radial Basis Function Networks

• Recurrent Neural Networks

• Convolutional Neural Networks

• Generative Adversarial Networks