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.
The machine learning approach consists of collecting large amounts of examples that have a particular output for a given input. The machine learning algorithm utilizes examples to produce a program for performing a complex job. Using machine learning is cheaper than hiring many human programmers to work on a complex code.
Here is a list of the top 5 types of neural networks that machine learning enthusiasts must be familiar with:
Table of Contents
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.
In this neural network, the hidden layers have no contact with the external world and this is the reason they are named as hidden layers. Feedforward Neural Network is designed to deal with data that has a lot of noise.
Moreover, Feedforward Neural Networks are the simplest types of artificial neural networks. And for minimizing the errors in the prediction backpropagation algorithm is used to update the values of weights. Few applications of Feedforward Neural Network include pattern recognition, computer vision speech recognition, sonar target recognition, face recognition, and data compression.
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.
Radial basis function neural networks have been largely implemented in power restoration systems to restore power in the shortest time. Other use cases of radial basis function neural network are time series prediction, classification, and system control.
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.
In the convolutional layer before the result proceeds to the next layer, the convolutional layer uses a convolutional operation on the input due to which the network can be much deeper and with fewer parameters.
As a result convolutional neural networks are very powerful for visual recognition, recommendation systems, and natural language processing. A convolutional neural network is comparable to the connection pattern between the neurons of the human brain.
Convolutional neural networks can likewise be used to identify paraphrasing and are applicable for image classification, anomaly detection, and signal processing as well. They can also be used in image analysis and recognition in the agriculture sector where the weather data is extracted from satellites for predictions.
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.
In each step, every node remembers some information obtained from the previous time step. Briefly e every node acts as a memory cell and remembers some information obtained from the previous step while computing and carrying out operations.
This way if a prediction is wrong the system self learns and works towards achieving the right prediction during the Backpropagation process. The Recurrent neural network is highly useful for text to speech recognition.
One drawback of this neural network is its slow computation speed and it cannot remember information for a very long time. Other use cases of Recurrent neural networks are machine translation, robot control, time series prediction, time series anomaly detection, music composition, and rhythm learning.
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.
The computation time of any neural network is dependent on the number of nodes and the connections between the nodes, and in Modular neural networks, the independent networks function independently. The computation speed is a result of neural networks not connected to each other. Training a modular neural network is extremely quick as each module can be trained independently and more precisely to master its task.
The best part of neural networks is that they are designed to work in a similar way as the neurons of the human brain work. And due to this they are capable of learning and improving more and more with increasing data and usage.
Also, the usual machine learning algorithms tend to stagnate their capabilities after a point however neural networks have the ability to improve with increased data and usage. And this is the reason that it is believed that neural networks will be the fundamental basis on which the next-generation artificial intelligence technology will be built.
This means there will be a rapid increase in job opportunities for this domain. Students or professionals who are looking forward to creating a career in this industry can enroll for upGrad Machine Learning Certified Courses. upGrad students get future-ready with 1:1 personalized mentorship by industry experts.
The certifications are from tier-1 universities. The programs are suitable for working professionals and can be managed well with an existing job. The students also receive dedicated support and placement assistance from upGrad.
Also Read: Top Machine Learning Models Explained
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.
The output of the first level is passed into successive levels to process the final output. All types of neural networks are highly adaptive to particular cases and learn very quickly. The applications of neural networks range from visual recognition to making predictions. Considering the potential of the technology and the growing demand, job opportunities are also anticipated to rapidly increase in near future.
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