9 Types of Neural Networks: Applications, Pros, and Cons

Deep learning and neural networks are major topics in computer science and the IT sector because they now offer the greatest solutions to many issues in speech, picture, and natural language processing. Recently, several papers have been released demonstrating AI that can learn to paint, make 3D models, design user interfaces (pix2code), and create graphics given text. There are a number of other fantastic things being done daily by utilizing neural networks. This article will teach you the fundamentals of neural networks and thoroughly explain how neural networks function. 

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What are Neural Networks?

Neural networks, a subset of machine learning and at the core of deep learning algorithms, are also referred to as artificial neural networks (ANNs) or simulation neural networks (SNNs). Their structure and nomenclature are modeled after the human brain, mirroring the communication between organic neurons. Computers can use this to build an adaptive system that helps them continuously improve by learning from their failures. As a result, artificial neural networks make an effort to tackle challenging issues like summarizing documents or identifying faces. 

We can classify and cluster data using neural networks, which can be viewed as a layer of clustering and classification on top of the data you manage and store. When given a labeled dataset to train on, they help classify data by putting unlabeled data into groups based on similarities between example inputs. In this article, we will further explore neural networks and its types. 

How Do Neural Networks Work?

Neural network-based machine learning algorithms typically do not require programming with precise rules defining what to anticipate from the input. Instead, the neural network learning algorithm learns by analyzing many labeled examples provided during training and by utilizing this answer key to determine what qualities of the input are required to generate the proper output. The neural network can start processing new, unknown inputs and effectively produce correct results once a sufficient number of examples have been processed. 

The results usually grow more accurate as the program gains experience and observes a wider range of instances and inputs. For neural networks to function properly, there are four essential procedures to follow: 

1. Patterns can be “remembered” by neural networks through associating or training. The computer will match an unfamiliar pattern with the closest match it has in memory if it is presented with that pattern. 
2. Putting information or patterns into categories that have already been established. 
3. Clustering or identifying a unique element of each data instance to classify it without additional context. 
4. Prediction, or the generation of anticipated outcomes utilizing pertinent input, even when the relevant information is not immediately available. 

Different types of learning in neural networks are supervised, unsupervised, and reinforcement learning. Let’s check the types of neural network architecture. 

Major Categories of Neural Networks

The following are some of the major categories of kinds of neural networks: 

1. Classification

Classification tasks, which call for labeled datasets for supervised learning, are often where neural networks excel. For instance, neural networks can quickly and consistently apply labels while identifying visual patterns in hundreds of images. They master the art of tackling difficult, perplexing problems through training. The neural network learns to discern the most crucial aspects by itself. Thus the data scientist is not required to provide traits to differentiate between dogs and cats. 

2. Sequence learning

Sequence learning is a machine learning category that uses data sequences as input or output. Text streams, audio files, video clips, measurements, and more are all examples of sequential learning. 

3. Function approximation

Function approximation is a technique for approximating an unknown underlying function using previous or current observations from the domain. A function is learned to be approximated by artificial neural networks. 

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What are the Different Types of Neural Networks?

The depth, number of hidden layers, and I/O capabilities of each node are a few criteria used to identify neural networks. Types of neural network models are: 

• Feedforward artificial neural networks. 
• Perceptron and Multilayer Perceptron neural networks. 
• Radial basis functions artificial neural networks. 
• Recurrent neural networks. 
• Modular neural networks. 

The following are the different types of neural networks. So, let’s check out the neural network types and uses: 

1. Perceptron

Layers of connected nodes make up a neural network. Every node is a perceptron, which resembles a multiple linear regression. The signal obtained by multiple linear regression is fed into a non-linear activation function via the perceptron.

Source: cloudfront

Applications: 

• Data Compression: Encoding, reorganizing, or otherwise altering data to make it smaller is known as data compression. In its most basic form, it entails re-encoding data using fewer bits than the original representation. 
• Streaming Encoding: The encoding technique whitens the real-valued input data given to the first hidden units of a fully-connected neural network, resulting in faster training. 

Advantages: 

• The Logic Gates AND, OR, and NAND can all be implemented by perceptron. 
• It provides us with more reliable bases for making decisions and improves our ability to anticipate various outcomes in considering the available data. 

Disadvantages:

• Due to the hard-limit transfer function, the output values of a perceptron can only take on one of two values (0 or 1). 
• Perceptrons can only categorize sets of vectors that can be separated linearly. 

2. Feed Forward Neural Network

Feedforward neural networks are among the most basic types of neural networks. Information is passed through several input nodes in one direction until it reaches the output node. The network may or may not include hidden node layers, which helps to explain how it functions.

Source: cloudfront.net

Applications:

• Pattern Recognition: Pattern recognition is the technique of recognizing patterns using a machine learning algorithm. Pattern recognition is data classification based on prior knowledge or statistical information taken from patterns and/or their representation. 
• Computer Vision: Computer vision is a branch of artificial intelligence (AI) that allows computers and systems to derive relevant information from digital photos, videos, and other visual inputs and then act or recommend on that information. 

Advantages: 

• A series of Feedforward networks can operate autonomously with a minor intermediary to ensure moderation. 

Disadvantages: 

• Not suitable for deep learning. 
• More variables to be optimized. 

3. Multilayer Perceptron

A multilayer perceptron is a fully convolutional network that creates a collection of outputs from a set of inputs. A directed graph connecting the input and output layers of an MLP is made up of multiple layers of input nodes. Enroll in KnowledgeHut’s Data Science course syllabus to kick-start your career in Data Science.  

Source: cloudfront.net

Applications: 

• Machine Translation: To estimate the likelihood of a sequence of words, neural network techniques are used in neural machine translation, a cutting-edge approach to machine translation. 
• Complex Classification: A group of quantitative techniques known as “complex classification” is used to examine the dynamics and structure of complex networked systems. 

Advantages: 

• The benefit of multilayer perceptron is that they can learn non-linear models and train models in real-time. 
• It can handle a lot of input data. 

Disadvantages: 

• Relatively challenging to design and manage. 
• Reasonably slow. 

4. Convolutional Neural Network

The neurons in a convolution neural network are arranged in three dimensions rather than the typical two-dimensional array. The convolutional layer refers to the top layer. Each neuron in the convolutional layer processes only a small portion of the visual field. Like a filter, input features are gathered in batches.

Source: cloudfront.net

Applications: 

• NLP: Natural language processing (NLP) is the branch of computer science—specifically related to artificial intelligence or AI that gives computers the ability to understand written and spoken words in the same way that humans do. 
• Anomaly Detection: The process of identifying outlier values in a sequence of data is known as anomaly detection. 

Advantages: 

• Fewer learning parameters than a fully linked layer 

Disadvantages: 

• Design and maintenance are difficult. 
• Reasonably slow. 

5. Radial Basis Functional Neural Network

A Radial Basis Function Network comprises an input vector, an output layer with one node for each category, a layer of RBF neurons, and a layer of RBF neurons. The classification process involves comparing the input to examples from the training set, where each neuron has a prototype stored.

Source: cloudfront.net

Applications: 

• Function Approximation: Function approximation is an approach for measuring an unknown underlying function that is unknown using previous or current observations from the domain. 
• Time Series Prediction: Making scientific projections based on data with historical time stamps is known as time series forecasting. It entails creating models through historical study, using them to draw conclusions and guide strategic decision-making in the future. 

Advantages: 

• Designing adaptable control systems is a great idea. 
• The recently created algorithm is presented for creating small RBF networks and carrying out effective training procedures. 

Disadvantages: 

• Because of the gradient issue, it is challenging to train. 
• The issue of vanishing gradients affects the neural network. 

6. Recurrent Neural Network

Recurrent neural networks are constructed to comprehend temporal or sequential data. RNNs improve their predictions by using additional data points in a sequence. To modify the output, they take in input and reuse the activations of earlier or later nodes in the sequence.

Source: cloudfront.net

Applications: 

• Image captioning: The process of creating a written description of an image is called image captioning. 
• Predicting stock market fluctuations: You can determine the future worth of business stock and other financial assets traded on an exchange by utilizing stock price prediction powered by machine learning. 

Advantages: 

• One benefit is the ability to model sequential data where each sample can be presumed to depend on previous ones. 
• Used to increase the pixel’s efficiency when combined with convolution layers. 

Disadvantages: 

• Problems with gradient vanishing and exploding. 
• Recurrent neural net training could be challenging. 

7. LSTM: Long Short-Term Memory

LSTM networks introduce a memory cell. They can handle data that has memory gaps. The time delay is a factor that may be taken into account when using RNNs. However, LSTMs should be used if our RNN fails when we have a lot of relevant data and want to extract important information from it.

Source: cloudfront.net

Applications: 

• Speech Recognition: Speech recognition software is a technology that can process natural language speech and convert it into readable text with high accuracy. 
• Writing Recognition: A computer’s capacity to recognize and understand understandable handwritten input from sources like paper documents, photos, touch screens, and other devices is known as handwriting recognition (HWR), also referred to as handwritten text recognition (HTR). 

Advantages: 

• We can choose from a wide range of LSTM parameters, including learning rates and input and output biases. 
• Memory bandwidth is needed to compute linear layers. 

Disadvantages: 

• They require significant resources and time to train and become suitable for real-world applications. 

8. Sequence to Sequence Models

Two Recurrent Neural Networks create a sequence-to-sequence model. In this case, a decoder processes the output while an encoder processes the input. Working simultaneously, the encoder and decoder can use the same parameter or a different one.

Source: medium.com

Applications: 

• Super Resolution: A deep learning technique called super-resolution convolutional reconstructs high-resolution images from low-resolution images. 
• Clothing Translation: It is crucial to quickly identify correlations between fashion items that can be mixed and matched in a Clothing Translation system. 

Advantages: 

• It is capable of processing several inputs and outputs simultaneously. 

Disadvantages: 

• This architecture has extremely little memory. 

9. Modular Neural Network

A modular neural network consists of several distinct networks that each carry out a specific task. Throughout the calculation process, there isn’t much communication or interaction between the various networks. They each contribute separately to the outcome.

Source: ars.els-cdn.com

Applications: 

• Stock market prediction systems: You can determine the future worth of business stock and other financial assets traded on an exchange by utilizing stock price prediction powered by machine learning. 
• Adaptive MNN: With an emphasis on the operation and inference of deep neural network models, an Adaptive Mobile Neural Network (MNN) is a compact mobile-side deep learning inference engine. 

Advantages: 

• Efficient. 
• Independent training. 

Disadvantages: 

• Moveable target issues. 

Thus, these are some of the main types of neural networks.

Limitations of Neural Networks

The following are the limitations of the neural network: 

• The “black box” nature of neural networks is perhaps their most well-known drawback. To put it plainly, you have no idea how or why your NN generated a particular result. 
• Although there are libraries like Keras that make creating neural networks relatively simple, there are instances when you need greater control over the specifics of the method, such as when using machine learning to solve a challenging problem that no one has ever done before. 
• According to their structure, artificial neural networks demand processors with parallel processing power. This makes the equipment’s realization reliant. 
• The main issue with ANNs is network behavior that cannot be explained. When ANN comes up with a probing solution, it doesn’t explain why or how. This makes the network less trustworthy.

Conclusion

Thus, there are different neural network models and different types of neural network architecture. Neural networks serve as the foundation for many applications that provide users with an autonomous robotic experience. The current systems need a lot of modification to comprehend operating conditions and provide desirable outputs. Many applications and challenges, including space exploration, that call for more sophisticated techniques to investigate the circumstances in which human testing is constrained. In these situations, it must change to offer workable results that can aid in the advancement of research. You can check out the various courses provided by Knowledgehut to become a Deep Learning expert by working on real-life case studies and developing your skills for a successful career.