Incorporating Deep Learning in Elementary Education

Incorporating Deep Learning in Elementary Education

One way to think of keeping up with advancing technology is knowing how quickly we can introduce these concepts to future generations.

Here are a few methods we can implement to incorporate deep learning in elementary education.

What is deep learning?

Simply put, Deep Learning is a subset of Machine Learning which tries to copy and apply the brain’s decision-making abilities.

To understand Deep Learning, we first need to understand what Machine Learning is.

Although both terms fall under the umbrella term artificial intelligence, and one is a subset of the other, there are subtle differences between the two that make one more preferred than the other today.

Machine learning

According to IBM's Learn Hub Module, machine learning is a machine’s ability to learn from large amounts of data. Using machine learning, computers can execute a variety of functions without data scientists and programmers explicitly programming them to do so.

Deep learning

Deep Learning can be understood as a more advanced and evolved form of machine learning.

It attempts to mimic how the human brain works. Its functioning is inspired by how neurons work in our brains. Thus, it is called an artificial neural network. Deep learning employs the use of many more (three or more) layers of neural networks than machine learning. The layers of algorithms are stacked on top of one another, creating a sort of a hierarchy according to increasing levels of complexity.

They essentially simulate the nature of the brain and its processing capabilities.

This stacked structure of algorithms allows Deep Learning to put data into clusters and deliver predictions with incredible accuracy.

What this means, in simple terms, is that this process limits inaccuracies and refines the output that will be far more accurate.

The process is similar to how we learnt new things when we were younger.

We must have started out believing anything that went on the roads was a car, not knowing all of a car’s features. Our parents would have had to guide us slowly to learn that a car generally meant an automobile with four wheels and not just everything that went on the roads.

Over time, through several examples and corrections, we learnt that different cars looked different but still fell under the general category named car.

Why do we prefer deep learning over machine learning?

Although the processes of how machine learning and deep learning algorithms work are similar, people are generally leaning towards the processing prowess of deep learning given its time and energy-saving automation.

Machine Learning employs structured and labelled data, where the programmer needs to feed highly specific data about something that the machine will eventually learn to identify.

The problem, however, is that such large amounts of labelled and structured data do not exist, which means that a programmer needs to manually label the data into certain sets.

Thus, the learning depends on manual intervention. This takes up a lot of energy and is time-consuming.

Deep Learning algorithms, however, can also make use of unstructured data (which is abundant) besides labelled data and process it. This removes the need for the machine to depend on the capabilities of the human mind.

We call this unsupervised learning because the algorithms can function faster and more accurately without any supervision.

For example, if there were a set of images of vehicles fed as input data, deep learning algorithms can automatically extract the most important features and distinguish a car from a bike.

However, Machine Learning algorithms would require some pre-programming by the programmer to identify some distinguishing features so that the algorithms can later automate the process.

Why is the knowledge of deep learning necessary for children today?

We begin to learn how to do things right from when we are born. In today’s tech-dependent world, children are growing up with all sorts of the advanced technology around them.

Today, practical applications of deep learning, AI and machine learning surround children in the form of digital voice assistants and chatbots.

Thus, to keep up with changing times and become smarter with growing advancements, we must learn how deep learning, and other components of AI, impact our lives.

Understanding how these systems work at a younger age can help children, who are the people of tomorrow, to incorporate more deep learning mechanisms in day-to-day life and develop more user-friendly technology that aids everyone.

Most children learn when they create, so what better way to get children to learn about Machine Learning and AI than letting them experiment?

How can we incorporate deep learning in elementary education?

Thanks to ever-advancing technology, children do not have to spend hours poring over complex material to understand how deep learning works.

Several tools on the internet can nurture this interest in children to develop a base understanding of AI, machine learning, deep learning and programming.

A great example is Scratch, a visual programming language, which was a product of the MIT Media Labs creations in 2007. It is a programming interface that is accessible by millions around the world to learn how to code.

Google has a whole website dedicated to enthusiasts of AI to conduct their experiments. These simple experiments allow users to explore machine learning through pictures and music.

Magenta, powered by TensorFlow, is an open-source Python library having several web-based applications that use music and images as data to train Machine Learning models. This is a fun and creative way to get children hooked to learning about the different aspects of ML and Deep Learning algorithms.  

Multinational tech giant IBM also has an online activity kit to help teach children about machine learning algorithms using a more hands-on approach.

In 2017, Dale Lane created Machine Learning for Kids, an online educational tool to help teach children about the workings of machine learning and AI by letting them take over and experiment. It also has a page that redirects users to several other educational resources for children to learn about AI.

Of course, if your children are visual learners, then the simplest method would be to show them interactive videos.

There is no end to the resources that are abundantly available on the internet and no age is too young to begin exploring the fascinating world of AI.

Also read: 3D Tech in Education (

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Quick queries for this insight

Why is the knowledge of deep learning necessary for children today?
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By teaching children the basics of deep learning, we can give them the tools they need to succeed in a rapidly changing world. Furthermore, deep learning can help children develop critical thinking and problem-solving skills. As our world becomes more reliant on technology, it is imperative that we prepare our children for the future by teaching them the basics of deep learning.

What are some of the resources that can kickstart deep learning for kids?
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There are numerous tools and websites available which can help children kickstart their journey towards Machine Learning and Deep Learning. Some of them which one can explore are Machine learning for Kids, Experiments with Google, Magenta, Scratch and IBM Online Activity Kit; the links to which are in the blog.

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