Deep Learning - Convolutional Neural Networks with TensorFlow - CNN Code Preparation

The video tutorial covers the implementation of convolutional neural networks (CNNs) for image classification using Tensorflow. It introduces datasets like Fashion MNIST and CFAR 10, explaining their significance and differences. The tutorial guides through building a CNN model, training, evaluating, and making predictions. It emphasizes the use of the Keras Functional API for cleaner and more flexible model design. The video also discusses data loading, data augmentation, and the importance of adhering to coding conventions in machine learning.

What is the primary difference between Fashion MNIST and the original MNIST dataset?

• Fashion MNIST includes images of clothing items.

• Fashion MNIST contains color images.

• Fashion MNIST has more samples.

• Fashion MNIST images are larger in size.

Why is CIFAR-10 considered more challenging than Fashion MNIST?

• It has larger images.

• It includes grayscale images.

• It has more classes.

• It contains color images.

What is the first step in building a CNN model as discussed in the tutorial?

• Building the model

• Loading the data

• Evaluating the model

• Training the model

What is a key advantage of using the Keras Functional API?

• It is faster to execute.

• It is easier to debug.

• It requires less code.

• It allows for more complex models.

In the Keras Functional API, how are layers treated?

• As functions

• As simple objects

• As dynamic variables

• As static methods

What is the purpose of the Conv2D layer in a CNN?

• To flatten the input

• To add dropout regularization

• To perform convolution operations

• To pool the input

What does the 'stride' parameter control in a Conv2D layer?

• The size of the filter

• The speed of the filter movement

• The number of output channels

• The type of activation function

What is the default padding option in a Conv2D layer?

• Zero

• Full

• Same

• Valid

Why might dropout regularization not be ideal in convolutional layers?

• It complicates the model architecture.

• It increases computational cost.

• It can obscure important patterns.

• It reduces model accuracy.

What is recommended when deciding whether to use dropout in convolutional layers?

• Avoid using dropout.

• Follow standard practices.

• Consult with experts.

• Experiment and test results.