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Relevance of Deep Learning in Education;

Deep learning is a subset of machine learning. Although it is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.
While a neural network with a single layer can still make approximate predictions. Also additional hidden layers can help to optimize and refine for accuracy.

Deep learning drives many artificial intelligence (AI) applications and services that improve automation. Also performing analytical and physical tasks without human intervention.
Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

If deep learning is a subset of machine learning, how do they differ? Deep learning distinguishes itself from classical machine learning by the type of data that it works with and the methods in which it learns.

Machine learning algorithms leverage data to make predictions. This means that specific features are from the input data for the model and organized into tables. This doesn’t necessarily mean that it doesn’t use unstructured data; it just means that if it does. However it generally goes through some pre-processing to organize it into a structured format.

Deep learning eliminates some of data pre-processing that is typically involved with machine learning.
Although These algorithms can ingest and process unstructured data, like text and images.

Furthermore, it automates feature extraction, removing some of the dependency on human experts. For example, let’s say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”, “hamster”, et cetera.
Deep learning algorithms can determine which features (e.g. ears) are most important to distinguish each animal from another.

In machine learning, this hierarchy of features is established manually by a human expert.

Then, through the processes of gradient descent and back propagations. Here  the deep learning algorithm adjusts and fits itself for accuracy, allowing it to make predictions about a new photo of an animal with precision.

Machine learning and deep learning models are capable of different types of learning as well. They are usually categorized as supervised learning, unsupervised learning, and reinforcement learning.

Moreover Supervised learning utilizes labeled datasets to categorize or make predictions; this requires some kind of human intervention to label input data correctly.

In contrast, unsupervised learning doesn’t require labeled datasets, and instead, it detects patterns in the data, clustering them by any distinguishing characteristics. Reinforcement learning is a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward.

In this article you will learn some of the relevance of deep learning in education;

1. It is accurate and precise:
Deep learning as a subset of machine language is very accurate and precise. In addition if you want to make the best of your learning experience with little or no mistakes and errors then deep learning is a great approach.
Also, It can help to complete any task within the shortest possible time.


2. It is cost effective:
One of the main benefits and relevance of any technology driven tool is that it helps to cut cost.
Any user either a teacher or student that employs the use of deep learning will be able to get their work done with little or no cost. Most users are also able to tailor down their work to only what is important to them. This helps to focus only on the necessary tasks.


3. It is versatile:
Deep learning will continue to evolve and become more beneficial to people. It can be used for multiple purposes which makes it to be very versatile. Although it entails the use of numerous tools and resources, The deep learning algorithms can also be used to correct errors in any data that a user needs to use to carry out a task.


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Author: Semira Ayeni.

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