Deep Learning - Crash Course 2023 - Implementing Multi-Class Classification Using Keras

This video tutorial covers solving a multiclass classification problem using the MNIST dataset. It explains how to load and explore the dataset, preprocess the data by flattening and scaling, and build a neural network using Keras. The tutorial also demonstrates training the model and evaluating its performance, achieving high accuracy.

What is the primary purpose of using the MNIST dataset in this tutorial?

• To test image compression algorithms

• To generate random images

• To classify handwritten digits

• To perform binary classification

How many images are there in the MNIST training dataset?

• 10,000

• 50,000

• 60,000

• 100,000

What is the size of each image in the MNIST dataset?

• 32x32

• 28x28

• 64x64

• 16x16

What preprocessing step is performed to convert the image data into a format suitable for neural networks?

• Normalization

• Augmentation

• Flattening

• Cropping

Why is it important to scale the pixel values of images between 0 and 1?

• To improve the accuracy of the model

• To reduce the size of the dataset

• To ensure faster training

• To prevent numerical instability

What is the purpose of one-hot encoding the labels in the dataset?

• To reduce the dimensionality of the data

• To improve the speed of training

• To convert categorical labels into a binary format

• To increase the number of labels

Which activation function is used in the output layer of the neural network?

• ReLU

• Tanh

• Sigmoid

• Softmax

How many units are there in the final layer of the neural network?

• 512

• 256

• 10

• 128

What is the batch size used during the training of the neural network?

• 32

• 64

• 128

• 256

What accuracy was achieved on the test data after training the neural network?

• 90%

• 100%

• 95%

• 98%