Fundamentals of Neural Networks - Lab 1 - Introduction to Convolutional 1-Dimensional

The video tutorial covers convolutional neural networks, focusing on 1D convolutional operations. It begins with an introduction to CNNs and the methodology for building them. The tutorial then demonstrates how to initialize input shapes and generate random numbers using Python and TensorFlow. It explains convolutional layers, including filters and kernel sizes, and provides a detailed example of a convolution operation. The session concludes with a preview of the next lab session, which will cover 2D convolution and deeper CNN architectures.

What happens to the dimensions when using a kernel size of 2 in a convolution operation?

How does changing the number of filters affect the output shape?

What will be the output shape if the input is a 5 by 10 grid and the kernel size is 2?