Fundamentals of Neural Networks - Lab 2 - Introduction to CNN

The video tutorial covers the architecture of neural networks, focusing on feedforward and backward propagation. It introduces convolutional neural networks (CNNs), explaining convolution operations and filter applications. The tutorial guides viewers through building a CNN using TensorFlow and Keras, detailing the steps to design, compile, train, and evaluate the model. The video emphasizes understanding the data, model architecture, and performance evaluation.

What is the primary role of the activation function in a neural network?

• To initialize weights

• To introduce non-linearity

• To reduce overfitting

• To normalize inputs

In backward propagation, what is primarily updated to minimize the loss function?

• Activation functions

• Output labels

• Weights

• Input data

What does a convolution operation primarily involve?

• Matrix inversion

• Element-wise matrix multiplication

• Matrix addition

• Matrix transposition

What is the purpose of using a filter in convolution operations?

• To convert images to grayscale

• To reduce noise

• To detect specific features

• To increase image size

How does a convolutional neural network differ from a regular neural network?

• It requires less data

• It uses convolutional layers to detect patterns

• It processes data in batches

• It uses more layers

What is the CIFAR-10 dataset primarily used for?

• Image classification

• Time series analysis

• Text classification

• Speech recognition

Why is it important to match the input shape of the data with the model's input layer?

• To ensure faster training

• To reduce data size

• To prevent errors during model execution

• To improve model accuracy

What is the role of the optimizer in training a neural network?

• To adjust learning rate

• To increase model complexity

• To initialize weights

• To minimize the loss function

Which activation function is typically used in the output layer for multi-class classification?

• ReLU

• Sigmoid

• Softmax

• Tanh

What does the 'epoch' refer to in the context of training a neural network?

• A single pass through the entire training dataset

• The number of layers in the network

• The size of the input data

• The number of neurons in a layer