Types of Neural Network Algorithms in Machine Learning (+ Real-World Examples)

Introduction

The goal of machine learning is to develop computer programs that can use data to learn by themselves. Machine learning achieves this by utilizing neural networks modeled loosely after the structure of the biological brain.

The human brain consists of a network of neurons responsible for creating new connections in the brain, thus creating new memories and recording learned information. These neural networks are also responsible for retrieving information and using it to recognize patterns.

What are Neural Networks in Machine Learning? 

Neural networks in machine learning refer to a set of algorithms designed to help machines recognize patterns without being explicitly programmed. They consist of a group of interconnected nodes. These nodes represent the neurons of the biological brain. 

The basic neural network consists of:

• The input layer
• The hidden layer 
• The output layer

Neural networks in machine learning use mathematical or computational models to process information. 

These neural networks are usually non-linear, which allows them to model complex relationships between data input and output and find patterns in a dataset. 

The application of neural networks in machine learning tends to take one of these three broad categories:

• Classification whereby a neural network can recognize patterns and sequences
• Functional approximation and regression analysis
• Data processing including clustering and filtering data

Using neural networks for machine learning has some advantages including:

• They store information on the entire network, meaning that the neural network can continue functioning even if some information is lost from one part of the neural network.
• Once neural networks are trained with a quality data set, they save on costs and time as they take a shorter time to analyze data and present results. They are also less prone to errors, especially if they are trained with high-quality data. 
• Neural networks provide quality and accuracy in results

What Type of Algorithm is a Neural Network? 

Several types of neural networks exist today. These neural networks are classified based on their density, layers, structure, data flow, and depth activation filters among other features. We are going to focus on three types of neural networks.

• Convolutional neural network (CNN)
• Recurrent neural network (RNN)
• Deep Neural Network (DNN)

1. Convolutional Neural Network (CNN)

For a long time, convolution neural networks were limited in their use due to scalability issues. These neural networks needed a lot of training data for efficiency, and they were only applicable for low-resolution images. Since 2012, however, AlexNet reintroduced multi-layered neural networks and used large data sets from the ImageNet data set, allowing for the creation of complex convolutional neural networks. 

What is a CNN?

A convolutional neural network (CNN) is a deep learning algorithm specifically designed to process image data. Convoluted neural networks are used in image recognition and processing.

The neural networks in a CNN are arranged similarly to the frontal lobe of the human brain, a part of the brain responsible for processing visual stimuli. 

The convolutional neural network consists of:

• A convolutional layer,
• A pooling layer,
• A fully connected input layer, 
• A fully connected layer, and
• A fully connected output layer

Unlike multi-level perceptrons, CN is sophisticated enough to apply filters that capture the spatial and temporal dependencies in an image. This way, CNN can provide higher accuracy, even with high-resolution images. 

The applications of convolutional neural networks include in

• Image recognition
• Video recognition 
• Image classification 
• Medical image analysis
• Image segmentation 
• Natural language processing (NLP)
• Recommender systems

How Does It Work 

Convolutional neural networks stand out in their ability to learn by themselves. This algorithm works by assigning learnable weights and biases to an image to distinguish it from other images while retaining the features critical for achieving a good prediction.

Images are represented as a matrix of pixels with different planes. A grayscale image consists of one plane while an RGB image consists of three planes. 

Now, in a convolutional neural network, there are multiple layers of artificial neurons, each with a mathematical function that calculates the sum of multiple inputs.

When an image is inputted in a CNN, the first layer extracts basic features of the image. These include the edges of the image. Once this layer extracts these basic features, the image moves to the next layer which detects more complex features such as combination edges. 

As the image moves through multiple layers of the convolutional neural network, more complex features are detected. At the classification layer, the algorithm assigns classes in which an image is more likely to belong. This is where the neural network will assign an image as a person, cat, horse, etc.   

Advantages of a Convolutional Neural Network 

The advantages of convolutional neural networks include:

• They can detect important features without human supervision 
• It has the highest accuracy amongst image detection algorithms
• It is easy to understand and implement 

Examples of CNN

Some of the real-world examples of CNNs in use include:

• Wildfire detection system by Sintecsys uses both daytime and nighttime images to check for the presence of wildfires and notify the relevant firefighting authorities. 

• CNN’s have also been used in medical settings to detect cancer and analyze x-ray images. 

2. Recurrent Neural Network (RNN)

What is an RNN?

A recurrent neural network (RNN) is an artificial neural network that uses sequential or time-series data to solve problems in speech recognition and language translation. RNNs have been used in:

• Language translation
• Natural language processing
• Speech recognition
• Image captioning

How does it work 

In recurrent neural networks, connections between nodes form directional or undirected graphs along a temporal sequence. These neurons have an internal memory, which makes RNNs the best neural networks for machine learning problems that involve sequential data. 

Sequential data includes financial data and DNA sequence which is ordered in a certain way. RNNs come in four types:

• One to one
• One to many
• Many to one
• Many to many

In recurrent neural networks, each input is dependent on the previous input. For example, in a voice search RNN application, each word entered relies on the previous word to form a coherent search query. 

As with other neural networks, RNN consists of the input, hidden, and output layers. The input layer fetches and preprocesses the data before feeding it to the hidden layers. The hidden later retrieves any useful information from the data before passing it to the output layer. 

In RNN models, each input is converted into a dependent variable and looped throughout the RNN layers where it is preserved in its memory. 

Advantages of Recurrent Neural Networks

RNNs has also their huge advantages, which are:

• Recurrent neural networks can remember previous input allowing them to accurately predict the next sequence
• They can be used with convolutional layers to extend their effective pixel neighborhood
• A larger input size does not increase the size of the model 

Examples 

Due to their relevance in speech-related tasks, RNNs have been used in applications such as:

• Google translate
• Siri
• Speech synthesis
• Voice search 
• Brain computer-interfaces
• Handwriting recognition
• Music composition 

You can find a machine learning tutorial on using recurrent neural networks for time series with code explained. You can also use it to train your model for various applications of RNN. 

3. Deep Neural Network (DNN)

What is a DNN?

A deep neural network (DNN) is an artificial neural network consisting of multiple layers between the input and output layers. These layers could be recurrent neural network layers or convolutional layers making DNN’s a more sophisticated machine learning algorithm. DNNs are capable of recognizing sound, creative thinking, recognizing voice commands, and analysis. 

How does it work

DNN is a type of machine learning algorithm that learns through repetitive action from many samples. When you feed a computer with a piece of information, the DNN sorts the data based on its elements, for example, the pitch of a sound. 

The data is passed through successive layers until it can accurately determine the type of sound made in the data. The model then receives feedback on the correct answer which strengthens its learning process. 

Advantages of Deep Neural Network (DNN)

Deep Neural networks also have its own benefits of use:

• DNNs are capable of learning the nonlinear mapping between inputs and outputs, and the underlying structure of the input data vectors
• DNNs are capable of self-learning
• They are scalable

Examples 

Deep neural networks have been used in applications such as self-driving cars, smartphones, drones, and games. Below you can find two comprehensive tutorials of projects from Omdena that have used deep neural networks:

• Weed vs crop detection to help reduce the use of herbicides in farming

• Crop yield detection using deep neural networks 

What is the Difference Between a CNN vs. RNN vs. DNN? 

Convolutional neural networks, recurrent neural networks, and deep neural networks are examples of algorithms used in machine learning. They, however, have some unique differences that make them ideal for different applications. 

So, how are these types of algorithms different from each other? 

Convoluted neural networks work well in computer vision applications by processing image data. Recurrent neural networks, on the other hand, work well with sequences of data. They are applied in forecast and language models. RNNs are also more computationally and memory intensive than CNNs. 

Both CNN and RNN are sometimes considered part of DNN. In CNN, the difference is that the layers are not interconnected as in a DNN. Another key difference between CNNs and DNN is that a DNN must have a minimum of two to three hidden layers. RNN can be considered a DNN due to its ability to process temporal data. 

Conclusion 

Neural networks are at the heart of today’s most powerful machine learning systems. Whether it’s identifying objects in images, understanding human speech, or powering deep learning breakthroughs across industries, each type of neural network—CNNs, RNNs, and DNNs—offers unique strengths for solving real-world challenges. With the right data and architecture, these models enable faster decision-making, higher accuracy, and intelligent automation across countless domains. As AI continues to evolve, neural networks will remain the foundation for building smarter, more impactful solutions.

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