Neural Networks for Machine Learning Engineers: Top 5 Types You Should Know

Neural Networks for Machine Learning Engineers: Top 5 Types You Should Know

in this article, will show Neural Networks for machine learning engineers: Top 5 categories you should know

When coding by hand becomes too complex and impractical for humans to handle directly then machine learning algorithms are required. A vast amount of data is fed to a machine learning algorithm and the desired output is set by the programmers. The algorithm works with the data and looks for the best model to achieve the set desired output.

Consider an example of such a complex situation. Recognition of a three-dimensional object from real life. Now writing such a program is not a cakewalk for programmers because we do not know how the process is done in our brains. And even if we are able to decipher how a human brain actually does the process, it might not be feasible enough for a human to program it due to its extensive complexity.

Top 5 Types of Neural Networks

*1. Feedforward Neural Network *

In Feedforward Neural Network all the nodes are fully connected and the data is passed through to different input notes till it reaches the output node. The data moves in a single direction from the first level to the output node. Here the sum of products of inputs and weights are calculated and then fed to the output.

2. Radial Basis Function Neural Network

Radial basis function neural networks have a fast learning rate and universal approximation. They are usually used for function approximation problems. They have two layers and are used to consider the distance of any point with respect to the center. In the inner layers, the features are united with the radial basis function, and the output from this first layer is considered for the computation of output in the next layer.

3. Convolutional Neural Network

Convolutional neural networks are largely used in analyzing visuals. They are highly accurate and follow a hierarchical model that works to build a funnel-like network that finally gives out a completely connected layer where all the neurons are connected and the output is processed.

4. Recurrent Neural Network

Recurrent neural networks are a variation of Feedforward Neural Network. In a Recurrent neural network, the output of one particular layer is fed back into the input. This process helps predict the outcome of the layer. The first layer formed is similar to the Feedforward network and in the successive layers, the Recurrent neural network process occurs.

5. Modular Neural Network

A Modular neural network consists of a series of independent neural networks that are moderated by an intermediary. The independent neural networks operate independently and execute subtasks. The different neural networks do not interact with each other during the computation process. And due to this large complex computational processes are done comparatively quicker as they are broken down into independent tasks.


Modern machine learning technology works on computational models that are known as artificial neural networks. Various types of neural networks follow similar principles as are nervous system in the human body. Neural networks consist of a large number of processes that are arranged in levels and operate together. In the first level, the neural network receives raw input that is similar to how our nerves receive inputs.

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