ANN vs. CNN – What Are The Significant Differences?
There are two major foundations to AI in the name of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). While both of them are revolutionary in their own sense and source from somewhat similar concepts, they do tend to accommodate entirely different characteristics — a fact that becomes a source of massive distinction when it comes to their capabilities, shortcomings, and possible applications.
As a result, it becomes imperative for the tech geeks to discern their definitions and carve out the distinctions so that these can be put to use in the most ideal way. Simply put, comprehension of their theoretical prowess allows for informed decisions when developing AI products.
But, before we get into the technicalities, let’s first clarify what exactly ANN and CNN are.
What are Artificial Neural Networks (ANN)?
ANN are artificially intelligent structures that simulate the fabrication of a human brain. The neural network takes in an input and processes them one by one in layers. Each layer is a mathematical formula that takes the output from the previous layer and profoundly processes it for making it ideal for use within the next layer. At heart, this network is made up of neurons where each neuron represents a core idea or factor that affects surrounding neurons.
The Revolutionary Advantages of ANN
1. In the real world, ANNs are used to address tasks that are extremely complex and operate in uncertain environments. For instance, they are extensively applied for tasks involving human language. An example of this is Google’s natural language processing (NLP) system which is artificially trained to enhance the company’s ability to detect spam messages on Gmail intelligently.
2. ANNs make it possible for computers to mimic the behaviours of the human brain very closely — something that is sure to significantly increase their accuracy and productivity as humans develop more complex computing environments. For instance, an ANN can assess how a person will feel when exposed to new stimuli based on their past experiences and actions, something that would require an incredible amount of laborious data gathering and analysis by conventional ML systems.
Disadvantages of ANN
1. It is hard to adapt ANN to specific tasks that do not have clear mathematical descriptions. As a result, it becomes difficult for them to deal with uncertainty — a scenario that often confronts the real world.
2. ANNs are relatively slow compared to conventional computational systems (considering that their computation power slows down as the data increases). They are also known to be very sensitive in nature, making them easily susceptible to computational errors that can significantly impact the overall performance of their applications.
What are Convolutional Neural Networks (CNN)?
Convolutional Neural Network (CNN) is short for “convolutional” (a verb describing the movement of “sinusoidal waves”) and “neural network.” The concept of the network itself still needs adjusting. However, existing neural networks are essentially nothing more than symbolic representations of high-level concepts. In other words, CNNs are made up of layers that have data arranged in a specific manner. For example, the topmost layer is the input layer that takes in a whole bunch of data and prepares it for further processing.
The Revolutionary Advantages of CNN
1. Traditional neural networks are not known to be practical since they have minimal capacity to cope with situations where large amounts of data are involved. However, CNNs solve this issue by taking in large amounts of information without slowing down. This is what makes them ideal for applications where humans can quickly gather a lot of data like text mining.
2. CNNs are also known to be very robust when dealing with data that has not been pre-processed. This is because they are designed with a high tolerance for error and can even work well with messy information.
Disadvantages of CNN
1. CNN can only be used to compute simple functions, unlike ANN that is functional in complex and multi-layered structures.
2. Also, the computational capacity of CNN is not known to be as efficient. This is because it takes a lot to train each layer of the network, creating a lot of computational overhead for them to function at their optimum capacity.
Core Differences Between ANN and CNN
As you might have already noted, the primary difference between ANN and CNN is their power to deal with data. In other words, the former can handle high uncertainty situations, while the latter is explicitly designed to function in the presence of heavy data. Apart from this, there are several other differences that all tech geeks must know:
1. Easy versus Difficult
CNNs are known to be easy to learn and implement compared to ANNs. It is not hard to design and get a general CNN to work. But, for you to achieve excellent results with your ANN, you need to be a genius with an in-depth understanding of their theory.
CNNs are faster than ANNs when it comes to dealing with large amounts of data. However, they are not as fast as typical computer systems if the data set is relatively small. The reason behind this is that they need more computational power to get worked up.
While CNNs are known to be relatively robust, ANNs are extremely sensitive. This is because the network they use to store data can easily suffer due to their lack of computational stability.
4. Resistive to Error
CNNs are resistant to errors while ANNs are not. This is because they can work in complex settings where there is a high tolerance for errors. Besides, ANNs are known to be more vulnerable when it comes to computations with significant uncertainties.
5. Type of Data
CNNs are designed to deal with visual data. However, ANNs are used to process non-visual data (tabular and text-based). This is because they have the ability to learn and adapt on their own without much human intervention.
It’s self-evident that CNNs are more valuable than ANNs when it comes to visually processing information, with the latter being more inclined towards processing tabular and textual data. Both constitute several advantages and disadvantages — the in-depth understanding of which is essential for developing efficient AI-powered solutions.
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