Deep Learning CNN Convolutional Neural Networks with Python - FashionMNIST Example Plan Neural Network

The video tutorial covers the process of building a neural network using TensorFlow and Keras. It begins with importing necessary libraries and loading the Fashion MNIST dataset. The data is then explored and preprocessed by scaling. A neural network model is constructed with layers, compiled, and trained over several epochs. The model's performance is evaluated by making predictions on test data. The tutorial concludes with a brief introduction to convolutional neural networks as a next step.

Which library is primarily used for building neural networks in TensorFlow?

• Pandas

• Matplotlib

• Keras

• NumPy

What is the shape of each image in the Fashion MNIST dataset?

• 64x64

• 128x128

• 28x28

• 32x32

How many classes are there in the Fashion MNIST dataset?

• 12

• 10

• 8

• 5

What is the purpose of scaling images in data preprocessing?

• To normalize pixel values

• To reduce image size

• To increase image size

• To change image format

Which activation function is used in the first hidden layer of the model?

• Sigmoid

• Tanh

• ReLU

• Softmax

What is the role of a dropout layer in a neural network?

• To prevent overfitting

• To increase the number of neurons

• To speed up training

• To decrease the number of layers

Which optimizer is used for compiling the model?

• Adagrad

• Adam

• RMSprop

• SGD

What does the 'epochs' parameter specify during model training?

• Number of layers

• Number of neurons

• Number of complete passes through the dataset

• Number of classes

What is the purpose of adding a softmax layer to the model?

• To change data format

• To reduce model size

• To convert outputs to probabilities

• To increase model complexity

What is the predicted class label for the first test example in the video?

• Ankle boot

• Trouser

• Sandal

• T-shirt