Fundamentals of Neural Networks - Lab 3 - Introduction to Neural Network

This video tutorial covers the basics of neural networks using Keras and TensorFlow. It begins with an introduction to neural networks and the Keras API, followed by data preparation using the FashionMNIST dataset. The tutorial then guides viewers through building a simple neural network architecture, compiling and training the model, and evaluating its performance. It concludes with experimentation and tuning of the model's parameters to optimize performance.

What is the primary purpose of using Keras and TensorFlow in this tutorial?

• To design a neural network architecture for a computer vision task

• To create a database management system

• To develop a web application

• To perform data encryption

What type of data is the Fashion MNIST dataset composed of?

• Audio data

• Image data

• Video data

• Text data

How are the images in the Fashion MNIST dataset represented?

• Grayscale images

• Binary images

• RGB color images

• Vector images

What is the purpose of the 'flatten' function in the neural network architecture?

• To change the image format

• To apply a filter to the image

• To increase the image resolution

• To convert a 2D image into a 1D array

Which activation function is used in the hidden layers of the neural network?

• Softmax

• ReLU

• Tanh

• Sigmoid

What is the role of the softmax function in the neural network?

• To perform binary classification

• To perform multi-class classification

• To increase the learning rate

• To reduce overfitting

What is the significance of the 'epoch' in training a neural network?

• It sets the initial learning rate

• It defines the number of times the entire dataset is passed forward and backward through the network

• It specifies the number of neurons in each layer

• It determines the number of layers in the network

Why is it important to use a separate test dataset for evaluating model performance?

• To reduce the size of the model

• To ensure the model is not overfitting to the training data

• To increase the training speed

• To improve the model's accuracy

What could be a potential consequence of setting the number of epochs too high?

• The model may underfit

• The model may overfit

• The model may not compile

• The model may lose data

Which of the following is a method to improve model performance?

• Increasing the number of hidden layers

• Decreasing the dataset size

• Using a single optimizer

• Ignoring the loss function