Deep Learning - Convolutional Neural Networks with TensorFlow - CNN for Fashion MNIST

The video tutorial guides viewers through a collab notebook for image classification using TensorFlow 2.0 and a convolutional neural network (CNN) on the Fashion MNIST dataset. It covers data preparation, model building with Keras functional API, and model training. The tutorial also discusses model evaluation, highlighting overfitting issues and analyzing predictions using a confusion matrix.

What is the primary purpose of the collab notebook discussed in the lecture?

• To explore natural language processing

• To demonstrate data visualization techniques

• To perform image classification using TensorFlow 2.0

• To analyze time series data

Why is it necessary to add a dimension to the input data for CNNs?

• To increase the number of samples

• To convert grayscale images to color

• To provide 3D input for convolution operations

• To reduce the computational complexity

How can the number of classes in the dataset be determined?

• By checking the model's output layer

• By counting the number of samples

• By using the set data structure to find unique values

• By analyzing the image dimensions

What is the advantage of using the Keras functional API over the sequential version?

• It automatically tunes hyperparameters

• It requires less memory

• It is faster to execute

• It allows for more complex model architectures

Which activation function is commonly used in the convolutional layers of the CNN?

• Softmax

• Tanh

• Sigmoid

• ReLU

What does an increase in validation loss with steady validation accuracy indicate?

• The model is underfitting

• The model is overfitting

• The model is perfectly trained

• The model has a data imbalance

What does the confusion matrix help identify in model evaluation?

• The number of layers in the model

• The areas where the model is confused

• The model's memory usage

• The speed of model training