Enmanuel Arias’ 750-Word Expanded Definition of Deep Learning


The purpose of this memorandum is to further elaborate on the definition of the term, deep learning, and discuss how it is defined and used by researchers and industry professionals. I will also be analyzing how the term is used contextually in across a variety of electronic publications. After analyzing how it is defined in other written works and used contextually, I will provide my own working definition of the term, deep learning, in relation to my major, computer system technology (CST).


“Deep learning systems are based on multilayer neural networks and power… Combined with exponentially growing computing power and the massive aggregates of big data, deep-learning neural networks influence the distribution of work between people and machines.”  (“Neural Network”, 2020) Although the Encyclopedia Britannica does not directly define the term deep learning, it explains the concept under the term neural network. While researching for definitions of deep learning, it was not uncommon to find deep learning and neural network being used together when defining what a neural network is. This is because deep learning is one of the methods neural networks use when analyzing data. Cho (2014) states that “…deep learning, has gained its popularity recently as a way of learning deep, hierarchical artificial neural networks.” (p. 15) This is further demonstrating the fact that deep learning can be defined as a way of helping neural networks learn deeply from the data it receives. In another instance, De (2020) defines deep learning as “…as a particular type of machine learning that uses artificial neural networks.” (p.353) In this particular definition of deep learning, we are made aware of the fact that deep learning is in fact a subset of machine learning, the ability for a computer to learn from data and adjust itself accordingly with minimal to no user input (“Machine”, 2020), that works in conjunction with neural networks to learn from the data inputted without the need for user intervention.


Now that we have explored a few definitions of the term deep learning, let us take a moment to see how the term is used contextually from a variety of sources. The IBM Corporation created a web page dedicated to explaining what deep learning is and its purpose. On this web page, the IBM Corporation (2020) states that, “Deep learning algorithms perform a task repeatedly and gradually improve the outcome through deep layers that enable progressive learning.” (para. 1) Here we can see how a reputable technology company describes how deep learning is able to learn from itself and improve its ability to interpret data more effectively. As the IBM Corporation stated, it is also important to note that deep learning is a progressive learning process and may take many iterations before it can effectively interpret large amounts of data without the need for user interference. In the ScienceDaily, a website dedicated to providing its visitors with the latest news on scientific discoveries from a variety of industries, we can see how the term deep learning is being used in the scientific research industry. In an article by the Institute of Science and Technology Austria (2020), it states that a group of international researchers from Austria, Vienna, and the USA, have developed a new artificial intelligence system that “…has decisive advantages over previous deep learning models: It copes much better with noisy input, and, because of its simplicity, its mode of operation can be explained in detail.” (para. 1) As the IBM Corporation had mentioned, deep learning is a progressive learning process and, in this case, the researchers mentioned in the article were able to further improve upon the current deep learning models to allow for better interpretation of input data. Chen (2018), a science reporter at The Verge, a multimedia technology news source, posted the transcript of an interview she had with Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies, in which he said “Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided.” (para. 1) It is important to note that there is a lot of hype surrounding machine learning, artificial intelligence and deep learning, and that a lot of the information that is readily available can be misinterpreted or as Sejnowski said “misguided”.

Working Definition

After reviewing the material I used to extract quotes from for the definition and context section of the memo, I will develop my own working definition of what deep learning means to me and it relates to my major, CST. I would define deep learning as an iterative learning method used by computers to interpret data inputted by a user without the assistance of the user.


Chen, A. (2018, October 16). A pioneering scientist explains ‘deep learning’. Retrieved October 26, 2020, from https://www.theverge.com/2018/10/16/17985168/deep-learning-revolution-terrence-sejnowski-artificial-intelligence-technology

Cho, K. (2014). Foundations of advances in deep learning [Doctoral dissertation, Aalto University]. https://aaltodoc.aalto.fi/handle/123456789/12729

De, A., Sarda, A., Gupta, S., & Das, S. (2020). Use of artificial intelligence in dermatology. Indian Journal of Dermatology, 65(5), 352–357. https://doi-org/10.4103/ijd.IJD_418_20

IBM Corporation. (2020, September 30). Deep Learning – Neural Networks and Deep Learning. Retrieved October 26, 2020, from https://www.ibm.com/cloud/deep-learning?p1=Search

Institute of Science and Technology Austria. (2020, October 13). New Deep Learning Models: Fewer Neurons, More Intelligence. Retrieved October 26, 2020, from https://ist.ac.at/en/news/new-deep-learning-models/

Machine. (2020). In OED Online. Retrieved from www.oed.com/view/Entry/111850.

Neural network. (2020). In Encyclopedia Britannica. Retrieved from https://academic-eb-com.citytech.ezproxy.cuny.edu/levels/collegiate/article/neural-network/126495

Summary of Parvanova’s “Explore Modern Responsive Web Design Techniques”

TO: Professor Ellis

FROM: Enmanuel Arias

DATE: September 16, 2020

SUBJECT: 500-Word Summary

This memo is a 500-word summary of the article, “Explore Modern Responsive Web Design Techniques” by Elena Parvanova, a member of the National Organizing Committee for the IEEE International Conference on Information Technologies.

29 years ago, Tim Berners-Lee created the first website that consisted of left-aligned text with blue hyperlinks on a white background. The first websites were created and managed by the IT departments of large companies. Nowadays, anyone with basic computer skills can create a website.  With the web design industry continually growing, it is important for companies to have well designed websites, as it can play a role in their success.

Web design began in 1993 with the introduction of images accompanied with text. In 1994, The World Wide Web Consortium was formed and established Hypertext Markup Language (HTML) as the standard for web design. HTML has its limitations, but the use of JavaScript resolves them. The following year, Flash and Cascading Style Sheets (CSS) were introduced. Flash became a popular tool to create more elaborate websites, but it was not search-friendly. Eventually, the combination of JavaScript and jQuery replaced the use of Flash. CSS provides a structure for designing multiple webpages. It allows websites to be created with a tableless design using percentages, known as fluid design.

With the increase of mobile devices with internet access, the layout of websites needed to adapt to the variety of screen sizes, while also keeping the design consistent across all devices. In 2007, column grid systems began to see widespread use by web designers. The most used system was the 960-grid system, with 12-column division. The system lays the content out on a 960px-wide browser window. Eventually, the fixed-width grid was replaced with percentages to align with fluid design.

Web designers had separate layouts for computers and mobile devices. Elena states that Ethan Marcotte is responsible for the birth of Responsive Web Design (RWD), who in 2010, “proposed that the same content could be used, but in different layouts and designed depending on screen size” (Parvanova, 2018, p. 3). RWD uses the viewport meta tag, grid system, and media queries to determine which layout to use when displaying content. RWD also led to the creation of responsive frameworks like Bootstrap. These frameworks standardized commonly used elements and introduced layout models like the CSS Flexbox and CSS Grid Layout.

Modern web design focuses on the organization of elements, positioning of blocks and the order of content. Flexboxes are optimized for interface design and the positioning of elements. The parent element will contain the child elements and “flex” accordingly to either fill unused space or shrink to prevent overflowing. Flexboxes were popularized because it allowed web designers to finally align elements properly. Unlike the grid layout, flexboxes are not intended to design the layout of an entire webpage. Since the grid layout is not as supported as flexboxes, a combination of the two is frequently used in RWD.


Parvanova, E. (2018). Explore Modern Responsive Web Design Techniques. Proceedings of the International Conference on Information Technologies, 43–48. Retrieved from http://infotech-bg.com/