Inspired by the structural and functional model of the human brain, neural networks, a branch of machine learning under the broader field of artificial intelligence, means a cluster of artificial neurons linked together to form a network, and then programmed to identify hidden patterns and relationships in datasets. In order to have a finer understanding of neural networks, it is judicious for one to learn Neural Networks from scratch.
The architecture of neural networks is anchored upon their operational concept known as the multilayer perceptron (MLP). The artificial neurons/nodes are arranged into three interconnected layers namely: Based on architecture and function, there are six major types of Neural Networks that we define below. The feedforward neural network is the most basic type of artificial neural network. As its name suggests, data movement only happens linearly and in a forward direction, therefore, lacking a feedback cycle between the layers. It is mostly useful in supervised learning where data samples do not have time or sequence-based variables. This type of neural network contains a feedback loop whereby it is possible for data to be fed back into the input layer for further processing before generating its output. In simpler terms, the recurrent neural network features a recurrent connection within its hidden layer which enables it to effectively and efficiently handle sequential or time-based data. Its major advantage is its parameter sharing ability. In other words, it is able to share parameters across different time steps. The convolutional neural network is most effective in signal and image processing as well as segmentation. Its architecture is founded on the concept of the visual cortex of the human brain. Thus, its operational concept is a powerful filtering system that is equipped to assign importance and handle features based on their relevance. The convolutional neural networks building blocks are in a sense filters known as kernels, whose major task is to simplify images or signals into easily processable forms without losing the vital features required to aid accurate prediction. The modular neural network is excellent for solving very complex problems that require a multidisciplinary approach. It is characterized by a collection of networks working independently, each having been built and trained for domain-specific tasks. It operates on the principle of separation and specificity purposely to reduce the complexity of the tasks. Breaking down the computation process increases operational efficiency by reducing redundancy. These independent networks are moderated by an intermediary which finally computes all their outputs into one comprehensive result. Named after Finnish professor Teuvo Kohonen, the Kohonen self-organizing network was originally developed to visualize metric vector distributions and to develop topologically ordered mappings. It is based on the principle of competitive learning to enhance output accuracy. The Kohonen self-organizing network fundamentally reduces the dimensions of data representation to as low as two, yet without losing its topological structure. The input data is processed and the output is generated based on similarity to the nodes. As a result, proximal clusters observations are more similar compared to observations in distal ones. The radial basis function neural network is simply a type of feedforward neural network with a slight variation in its hidden layer in that it uses the radial basis function as its activation function. Here, the given sample function is approximated using the sums of the Radial basis functions whose values depend on the radial distance between the input and some fixed point, often the center. What gives this network an edge over others is its impressive learning speed as well as its conventional approximation. The most significant benefits of Artificial neural networks for pattern recognition and other applications. Like all other technologies, ANN neural does not lack its disadvantages. These include: At the end of the day, neural networks as a field need more attention globally for their potential to be fully unlocked. The various types of artificial neural networks are powerful problem solvers with numerous applications in the real world across most if not all industries. This seemingly underexplored technology carries immense potential to transform the way businesses run their operation.The Fundamentals of Neural Networks
Types of Neural Networks
Advantages of Neural Networks
Disadvantages of Neural Networks
Conclusion