Deep Learning Definition, Comparison, Models, Examples

Deep learning is an artificial intelligence functionality that uses artificial neuron networks to mimic human learning patterns. It works by collecting, analyzing, classifying and implementing multiple datasets in real-time. This article discusses deep learning definition, comparison, working principle, models, and examples, as follows;




-Deep Learning Definition: 4 Ways to Define Deep Learning

-Deep Learning Vs Machine Learning

-How Deep Learning Algorithm Works

-Deep Learning Models

-Deep Learning Examples







Deep Learning Definition: 4 Ways to Define Deep Learning

Deep learning is an algorithm-based technology that uses large amounts of digital data to simulate human-like learning mechanism in software systems [1].

The concept of deep learning falls under artificial intelligence application. It is a variant of machine learning, which is one of the known branches of artificial intelligence.

A clearer account of the mechanism involved, can provide some insight into how deep learning works. Below is an alternative deep learning definition that briefly describes its mechanism and working principle;

Deep learning is the technology and process of progressive data analysis, and algorithmic pathway development, whereby neural network algorithms are developed based on successive layers of digital data that are fed into a software system, so that the system is able to learn by identifying repetitive patterns in the datasets [3].

It is called deep learning because it involves multiple dataset layers and in-depth analysis.

Next, the types of deep learning are used as a central theme to outline the deep learning definition;

Deep learning is an artificial intelligence functionality that uses datasets to develop neural networks of various types such as; multilayer perceptrons (MLPs), recurrent neural networks (RNNs), convolution neural networks (CNNs), and long short term memory networks (LSTMs); which analyze these datasets to identify patterns that can be interpreted and used to solve real-world problems [2].

The final deep learning definition given below, highlights some of the real-world uses of deep learning;

Deep learning is a software technology that is used in virtual assistants, natural language processors (NLPs), electric vehicles, and utility-based robotic systems to collect, analyze, interpret, and actively utilize multiple datasets to perform tasks.





Deep Learning Vs Machine Learning

Deep learning differs from machine learning in scope, approach, datasets, and functionality.

The main difference between deep learning and machine learning is that machine learning is a broad concept that represents the branch of artificial intelligence concerned with autonomous data-based pattern recognition; while deep learning is a subset of machine learning that focuses on the use of intricate neural networks to perform in-depth analysis of large data volumes.

Therefore, deep learning is considered machine learning with a significant level of complexity. On the other hand, the term; ‘machine learning’ is often used to describe analysis and correlation of data with fewer layers and lower complexity.

Using deep learning instead of machine learning is recommendable where the task requires analysis and comparison of a continuous stream of data input.

Because of its lower complexity, machine learning generally works better with conventional computer systems than deep learning. Also, machine learning algorithms are most effective when incoming datasets are structured or organized, while deep learning algorithms may use both structured and unstructured datasets.

Lastly, machine learning does not always involve intricate neural networks, whereas such networks are always present in deep learning.


Below is a table that summarizes the difference between deep learning and machine learning;

Comparison Criteria

Deep Learning

Machine Learning

Relative scope



Specification level



Relative complexity



Relative application range



Data versatility







How Deep Learning Algorithm Works

The algorithms that deep learning uses, are those which make it possible to collect and analyze large datasets on a simultaneous and in-depth basis; such as Convolutional Neural Network (CNN) algorithm.

Deep learning works through a three-step process comprising of data collection, analysis and classification, and implementation.

In data collection, the deep learning algorithm is fed with multiple datasets from various sources.

In most cases, these datasets have a common, unifying factor or component that can be used to draw links of similarity among them.

Link identification among datasets is what occurs during analysis and classification.

Basically, the deep learning algorithm begins to identify areas of similarity between multiple layers of data, and uses these similar attributes to classify the datasets into definite groups.

The identification of similarity-based links, and classification of data, forms the basis of neural network fortification.

Artificial neural networks function in similar manner to biological neurons in the brain; by compiling information from processed data, and specifying points of familiarity that link the information from one dataset to that from another [4].

The links between artificial neurons may be referred to as nodes.

Implementation is the final stage in deep learning, and occurs when information is used to make predictions of outcomes in real-world scenarios.

These predictions enable the deep learning system to make decisions in real-time, in a manner that is similar to human decision-making.

The decision-making of deep learning algorithms can be used in robotic navigation to avoid obstacles; in self-driven vehicles to recognize street features, in sustainable irrigation systems to predict soil and weather conditions, and in chatbots to conduct conversation, among others.

How Deep Learning Algorithm Works: Robotic Navigation as an Outcome of DL Implementation (Credit: Built Robotics, Inc 2021 .CC BY-SA 4.0.)
How Deep Learning Algorithm Works: Robotic Navigation as an Outcome of DL Implementation (Credit: Built Robotics, Inc 2021 .CC BY-SA 4.0.)





Deep Learning Models

A deep learning model is a neural network assemblage with a distinctive, identifiable architecture.

Deep learning models can therefore be identified based on their configuration, and can be described as linear, convoluted, regular, and cyclic, among others.

While the two concepts are referred to interchangeably in some scenarios, deep learning models are different from deep learning algorithms, because the latter is simply a predefined set of instructions while the former is a developmental, data-based feature formed as the DL system operates, based on the algorithm.

However, the deep learning algorithm plays a role in determining the specific architecture of a deep learning model. For example; the CNN is not a deep learning model, but may produce a convoluted model when used to control deep learning operations.





Deep Learning Examples

Deep learning examples include;

1). Geospatial data usage for automated vehicular navigation

2). Virtual assistant/chatbot data-based conversations

3). Facial recognition using pre-analyzed image(s)

4). Automated bioanalysis in medicine






Deep learning is the collection, analysis, linkage, classification and implementation of multiple data layers in a manner similar to human learning processes.


Deep learning differs from machine learning by being more function-specific, data-versatile, and complex.


Deep learning works by

1. Data collection

2. Analysis and classification

3. Implementation


Deep learning models are neural network systems defined based on their architecture, as linear, convoluted, and cyclic, among others.


Deep learning examples are; geospatial data usage for automated vehicular navigation, virtual assistant/chatbot data-based conversations, facial recognition using pre-analyzed image(s), and automated bioanalysis in medicine.






1). Agbaraji, C. E. (2019). “Deep Learning Technology: A Vital Tool for National Development.” International Journal of Engineering and Technical Research V8(07). Available at: (Accessed 1 January 2023).

2). Alzubaidi, L.; Zhang, J.; Humaidi, A. J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M. A.; Al-Amidie, M.; Farhan, L. (2021). “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.” J Big Data. 2021;8(1):53. Available at: (Accessed 2 January 2023).

3). Jahangeer, N.; Sivakamasundari, G.; Begum, A. A. S. (2022). “A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN.” Wireless Personal Communications. Available at: (Accessed 1 January 2023).

4). Weiss, R.; Karimijafarbigloo, S.; Roggenbuck, D.; Rödiger, S. (2022). “Applications of Neural Networks in Biomedical Data Analysis.” Biomedicines. 2022 Jun 21;10(7):1469. Available at: (Accessed 2 January 2022).

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