Convolutional Neural Networks, New Deep Learning Models

Convolutional Neural Networks, New Deep Learning Models

As Artificial Neural Networks (ANNs) are structured in a manner that mimics the various layers and functions of the human brain, software developers can configure these deep learning algorithms in a number of ways. This being said, Convolution Neural Networks (CNNs) are a class of neural networks that are most commonly used to identify and analyze visual imagery. To this point, the layers within CNNs are designed to imitate the ways in which human beings use their eyes to conceptualize the visual world around them. Likewise, CNNs have been implemented in a multitude of software applications that utilize computer vision, including facial recognition software and Optical Character Recognition (OCR), among a host of others.

How do CNNs work?

Just like any other form of an artificial neural network, convolutional neural networks are comprised of multiple layers of artificial neurons, just like the human brain contains millions of neurons that enable human beings to speak, walk, and view things with their eyes, etc. To this end, each layer within a CNN will be geared towards identifying one set of characteristics that make up a particular physical object. To illustrate this point further, consider a CNN that has been created to detect cars within photographs. The first layer of this CNN, known as the visible layer, would contain the individual pixels that make up the image of a particular automobile, as this data would represent the initial input of the overall deep learning model.

Alternatively, the second layer, known as the 1st hidden layer, will be geared toward identifying the edges that make up an image that depicts a car. Subsequently, the third layer within the CNN, known as the 2nd hidden layer, will then be used to identify the corner and contours of an image that depicts a car. Moreover, the fourth layer within the CNN, otherwise known as the third hidden layer, would be used to identify the physical parts that make up an object, such as the wheels, license plate, windows, and numerous other components that make up an automobile. Finally, the fourth layer of the CNN, known as the output layer, will be able to recognize that the object that has been depicted in the picture is indeed a car, in accordance with the previous four layers in the CNN.


As the name suggests, convolution neural networks are based on the mathematical operation of convolution. In the context of mathematics, a convolution is an operation on two individual functions that is used to produce a third function. In other words, the process works to blend one function with another. This third function will express the manner in which one shape has been modified by another, and the term convolution refers to the result of this function, as well as the process of computing said function. In keeping with the identification of physical information and objects within mediums such as images and video recordings, the layers within a CNN will be trained to identify the individual patterns that comprise a particular object, and the combination of these layers will enable the algorithm to identify the object itself.

Advantages and disadvantages of CNNs

Much like any other form of artificial intelligence or machine learning, there are both advantages and disadvantages to the application of convolutional neural networks. Beginning with the advantages, one of the most notable benefits of using a CNN is the level of accuracy that such models can achieve when it comes to identifying visual objects and information. On top of this, CNNs are still able to remain incredibly accurate when dealing with large amounts of data, compared with other forms of machine learning algorithms that see a decrease in accuracy when processing massive amounts of information of data. As computer vision programs are largely based on being able to identify the occurrence of a physical object within a particular medium in an accurate and efficient manner, there are very few rivals to CNNs in this respect.

On the other hand, of the primary disadvantages of CNNs is the sheer amount of data that is required to effectively train such models. This is a common issue with the development of many machine learning algorithms, as these models need to be fed a consistent stream of information in order to function correctly. However, due to the costs associated with obtaining large sets of data or information, as well as the man-hours required to label and implement these data sets, many software developers will not have the resources needed to reap the benefits of CNNs. Furthermore, CNNs are also more prone to adversarial attacks when contrasted with other machine learning, as the numerous layers within CNNs provide cyber criminals with additional opportunities to poison the data that is used to train such networks.

From object detection to speech recognition, the creation of convolutional neural networks has completely revolutionized the ways in which software develops and businesses can leverage deep learning algorithms to create new software programs, products, and services. Due in large part to the ways in which artificial neural networks can be structured, these algorithms have the power to identify physical objects and information with a wide range of mediums in a fashion thought to be impractical only a few decades ago. Because of this, CNNs will continue to be used to create new cutting-edge software applications, as developers have only scratched the surface of what this memorizing technology can truly do.

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