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 purpose of a convolutional neural network?

Explain the concept of feedforward neural networks.

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

Describe the process of backpropagation in neural networks.

What is the significance of the loss function in training a neural network?

How does the convolution operation work in a convolutional neural network?

Explain the concept of the CFAR 10 dataset used in the training process.

What is the purpose of pooling layers in a convolutional neural network?

What is the function of the softmax activation in the output layer?

How do you evaluate the performance of a trained neural network model?