Deep Learning - Computer Vision for Beginners Using PyTorch - Data Visualization

The video tutorial demonstrates how to load data in batches, visualize it, and convert tensor data to numpy format for easier manipulation. It explains the process of transposing image data to the correct format for visualization and how to display images using matplotlib. Additionally, a custom function is created to automate the process of displaying images from tensor data.

What is the purpose of loading data in batches of four with shuffling enabled?

• To ensure data is processed in a specific order

• To increase the size of the dataset

• To reduce the number of images processed

• To improve the randomness and variety of data during training

Why is it necessary to convert image data from tensors to numpy arrays for visualization?

• Numpy arrays are faster to process

• Tensors cannot be displayed directly

• Numpy arrays have a better resolution

• Tensors are not compatible with matplotlib

What is the correct order of dimensions for displaying an image using matplotlib?

• (3, 32, 32)

• (32, 32, 3)

• (32, 3, 32)

• (3, 3, 32)

Which function is used to change the order of dimensions in a numpy array?

• resize

• reshape

• flip

• transpose

What is the main advantage of creating a custom function for image display?

• It allows for batch processing of images

• It increases the image quality

• It automates the conversion and display process

• It reduces the code complexity