Give appropriate attribution for externally sourced media or code : Importing the relevant packages and load the data

The video tutorial covers the process of importing necessary libraries like Numpy and Tensorflow, and loading the MNIST dataset using Tensorflow's data provider. It explains how to store the dataset on different operating systems and introduces tweaks for loading data with additional arguments. The tutorial concludes with a brief on the next steps involving data preprocessing.

Which libraries are imported at the beginning of the exercise?

• Numpy and TensorFlow

• Keras and PyTorch

• Pandas and Scikit-learn

• Matplotlib and Seaborn

What is the primary purpose of using TensorFlow's data provider in this exercise?

• To perform data augmentation

• To load the MNIST dataset

• To visualize data

• To train a neural network

Where is the MNIST dataset stored by default on a Windows system?

• C:\Windows\System32

• C:\TensorFlow\Datasets

• C:\Users\YourUsername\TensorFlow Datasets

• C:\Program Files\TensorFlow

What does setting the 'as_supervised' argument to true do when loading the dataset?

• It increases the dataset size.

• It enables data augmentation.

• It changes the dataset format to CSV.

• It loads the dataset in a 2-tuple structure of input and target.

Why is it suggested to run the cell the first time you load the dataset?

• To ensure the dataset is downloaded and cached locally.

• To verify the dataset integrity.

• To perform data preprocessing.

• To visualize the dataset.