For example, if we have a (1K, 1K) image in RGB, then our input layer would take about 3 million inputs. If we have 1K units in the second layer, the corresponding weights will have (1K, 3M) dimensions. Training 3B parameters requires a lot of computational power and a large amount of data to be used for training.
Convolutional neural networks incorporate convolution operation on the input layer to reduce the dimensionality and provide the neural network with more valuable features of the data.

Example of Advantages of Using Convolutional Neural Networks for Image Recognition

- **Parameters Sharing**: a feature that is detected as useful in part of the image is probably useful in other parts as well.
- **Sparsity of Connections**: in each layer, each output value depends only on a small number of inputs, rather than considering all the pixel inputs.

University of Michigan - Ann Arbor

Google

One of the most popular introductory applications of CNN is image recognition, specifically sorting through images and classifying them as either cat or not cat.

Using Convolutional Neural Network for Image Recognition

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Advantages of Using Convolutional Neural Networks for Image Recognition

Edge detection is a fundamental image processing application of convolutional layers. It involves locating the boundaries of an object within an image by finding the precise locations of pixel changes. By performing a cross-correlation or convolution operation with a specifically constructed filter matrix, or kernel, the layer can successfully detect distinct kinds of boundaries, such as vertical or horizontal edges.

Learn Before

Related

Learn After