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

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)

Edge detection is a basic image processing technique that helps to find the boundaries of objects in an image by detecting the sharp changes of brightness. It has been widely used in many fields, such as finding fingerprints of tumors and self-driving cars.

One of the applications of the convolution operation in CNN is edge detection. Tuning the parameters of the filter matrix (kernel) allows detecting distinct kinds of boundaries in the image, such as vertical and horizontal edges.

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