Deep Learning - Deep Neural Network for Beginners Using Python - Splitting the Data (NN Implementation)

The video tutorial explains how to perform random sampling and split data into training and testing sets using Numpy. It covers the process of selecting random indices from a dataset, allocating data to training and testing sets, and adjusting the train-test split ratio. The tutorial also discusses separating features and labels for training purposes, ensuring a clear understanding of data preparation for machine learning tasks.

What is the purpose of using Numpy's random choice function in data processing?

• To sort the data in ascending order

• To randomly select indices for data splitting

• To calculate the mean of the dataset

• To remove duplicates from the dataset

Why is it important to ensure the correct distribution of data between training and testing sets?

• To increase the size of the dataset

• To ensure the model is trained on diverse data

• To reduce the computational cost

• To make the dataset more complex

What are some common train-test split ratios mentioned in the tutorial?

• 100-0, 90-10, 80-20

• 50-50, 60-40, 70-30

• 90-10, 80-20, 70-30

• 95-5, 85-15, 75-25

What is the main reason for separating features from labels in a dataset?

• To ensure the model learns from the correct data

• To improve the accuracy of the model

• To reduce the size of the dataset

• To make the dataset easier to visualize

What is the next step after preparing the data for training and testing?

• Collecting more data

• Running the model

• Writing helper functions

• Visualizing the data