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I think one thing that might help... look at it like this. A standard densely connected layer can represent a convolutional layer (standard'under the hood' implementation of convolution layers even converts it into a dense layer in a lot of cases so it can leverage fast matrix operations). In theory, if the dense layer can represent a convolutional layer, why's the convolutional layer used instead? You could just say that it's because there's less parameters, but it goes deeper than that. It'makes an assumption' that things likely to be seen in the dataset should be translation equivarient.

The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we see today in the field of artificial intelligence (AI) and Big Data. With this in mind, a great deal of thought and research has gone into working out the best way to store and organize information during the digital age. The relational database model was developed in the 1970s and organizes data into tables consisting of rows and columns – meaning the relationship between different data points can be determined at a glance. This worked very well in the early days of business computing, where information volumes grew slowly. For more complicated operations, however – such as establishing a relationship between data points stored in many different tables - the necessary operations quickly become complex, slow and cumbersome.

The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we see today in the field of artificial intelligence (AI) and Big Data. With this in mind, a great deal of thought and research has gone into working out the best way to store and organize information during the digital age. The relational database model was developed in the 1970s and organizes data into tables consisting of rows and columns – meaning the relationship between different data points can be determined at a glance. This worked very well in the early days of business computing, where information volumes grew slowly. For more complicated operations, however – such as establishing a relationship between data points stored in many different tables - the necessary operations quickly become complex, slow and cumbersome.