Create a computer vision system using decision tree algorithms to solve a real-world problem : Implementing CNN's in Ker

The video tutorial explains how to build Convolutional Neural Networks (CNNs) using Keras, focusing on data preparation, specialized layers, and architecture configuration. It covers the importance of data dimensions, color channels, and the use of layers like Conv2D, Conv1D, and Conv3D. The tutorial also discusses flattening layers, perceptrons, and the challenges of data collection and training. It introduces specialized CNN architectures like LeNet, AlexNet, GoogleNet, and ResNet, highlighting their unique features and performance optimizations.

What are some hyperparameters that can be tuned in a CNN?

What challenges are associated with obtaining data to train a CNN?

How does the Resnet architecture improve performance in CNNs?