The simplest form of ANNs, known as recurrent neural networks (RNNs), are represented as hierarchical organizations of neurons. Each neuron passes a signal or message to other neurons to form a complex network that learns through a feedback mechanism. In most vision and speech recognition applications, this type of ANN is used, as it is fast and works well for small datasets.
The differences between RNNs and CNNs can be found in their capabilities. While RNNs are trained to handle text and images, CNNs are trained to distinguish between contrasting features. Because CNNs use a grid of points to represent a shape or pattern, they are better suited to recognize images. In general, CNNs are faster and have more advanced capabilities, making them the superior choice for image processing.
During training, each neuron processes input data by extracting a feature. The neuron’s weights determine how much the feature is processed. When the neural network reaches the output layer, it applies the most likely label. Each layer contains one or more neurons, and each neuron’s activation function determines its role. This combination of neuron-based learning algorithms performs a transformation described by a common function F.
Another important difference between a simple neural network and a deep neural network is their use cases. The latter can handle unlabeled data, which comprises the vast majority of the world’s data. This is known as raw media, and deep learning can process it and cluster it into categories, which can be useful for a variety of tasks. It also has the added benefit of identifying similarities and patterns between images.
A Deep Net model can make generalizations by itself, which is another important advantage. It stores these generalizations in its hidden layer. Because the value of “black box” is so abstract and hard to explain, it is not easy to investigate. On the other hand, a simple net can be explained in terms of teacher rubrics. In addition to the differences between the two types of neural networks, deep nets are more efficient than their simple counterparts.
The difference between a simple and deep neural network is often a matter of complexity. The earliest neural networks were shallow, with only one hidden layer and one input and output layer. In contrast, a deep neural network contains many layers. In terms of complexity, the difference is not so much about the number of layers but about the depth of the network. The depth of a recurrent neural network is typically more than three layers.
While simple networks are more efficient when it comes to training a deep neural network, the lack of control over training makes them less reliable. The biggest disadvantage of deep networks is that they are more difficult to test and can’t be fully tested on their outputs. However, this fact does not mean that deep networks are useless. A deep network is still much better than a simple one when it comes to learning and applying it in a commercial setting.