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What Is Deep Learning? An Easy to Understand Guide


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In this article, you will read more about what deep learning is and why it is valuable to businesses across industries. Particularly, you will learn about:

  1. The history of the technology
  2. Definition of the term deep learning
  3. Why deep learning is important for businesses
  4. Examples of real-world deep learning applications

About us: At viso.ai, we power the end-to-end computer vision platform Viso Suite. The solution enables leading companies to build, deploy and scale their deep learning applications. Request a demo for your organization.


What Is the History of Deep Learning?

Since the 1950s, a small subset of Artificial Intelligence (AI), often called Machine Learning (ML), has revolutionized several fields in the last few decades. Neural Networks form a subfield of Machine Learning, and it was this subfield that spawned Deep Learning.

Deep Learning is a class of ML developed largely from 2006 onward and has since been driving disruptions in almost every application domain. Learning is a procedure consisting of estimating the model parameters so that the learned model (algorithm) can perform a specific task. For example, in Artificial Neural Networks (ANN), the parameters are the weight matrices. On the other hand, Deep Learning consists of multiple layers in between the input and output layer, which allows for many stages of non-linear information processing units with hierarchical architectures for feature learning and pattern classification.

In the last ten years, forms of Deep Learning have gained traction as a valuable commodity for tech industries. Companies use Deep Learning technologies to develop Social Media detection algorithms (Meta/Facebook using Face Recognition) and perform tasks such as automated driving (Tesla) or auto-captioning of videos (YouTube) and to perform medical image analysis.


Face Detection with Deep Learning Methods
Deep learning for face detection

What Is the Definition of Deep Learning?

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. According to the dictionary, Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision-making.

Hence, Deep learning is a sector of machine learning methods based on artificial neural networks with representation learning. Because the computer gathers knowledge from experience, there is no human needed to operate the computer and specify the knowledge needed by the computer. The hierarchy of concepts allows the computer to autonomously learn complicated concepts by building them out of simpler ones. Therefore, a graph of these hierarchies would be many layers deep (hence the name deep neural network).

In simple terms, Deep learning is a software technology used by programmers to teach computers to do what humans have been doing since the beginning of time: learning by example – or receiving, processing, and filtering complex information with all five senses to produce a final output. The models train off layered algorithms in order to achieve a specific goal.

Classification of Computer Vision and how it relates to Deep Learning
What is deep learning? Classification of Deep Learning and how it relates to Artificial Intelligence and Computer Vision.

How Does Deep Learning Work?

Deep learning models rely on layers of artificial neural networks (rather than inputted data) to train from programmed instances of features or distinctions. These multilevel layers allow models to detect and train from their own mistakes. Within the hierarchy of programmed algorithms, each contains its own concept for the model to search for, allowing it to validate its own outputs (training vs. inferencing).

A machine learning model, on the other hand, would produce errors or low accuracy rates when the given structured data is not sufficient. However, deep learning models do produce wrong classifications when the algorithms do not specify the features clearly enough.

Hence, deep learning requires the data collection of relevant and high-quality training data. In addition, the collected data needs to be annotated to provide the ground truth for the model to learn. In computer vision, data annotation involves image annotation or labeling.

computer vision image annotation cvat in Viso Suite
In computer vision, the training data for deep learning models are created through image annotation. – Source Viso Suite


Why Is Deep Learning So Popular?

Deep learning, due to its ease of implementation and knack for efficiently solving problems, is becoming increasingly valuable to companies. Given its custom attributes, the algorithms behind it are worth a lot today. Creating an algorithm that can solve a distinct, new problem boosts the value of a product that incorporates it. Because of the uniqueness of novel algorithms, companies that create them often generate massive profits.

For example, Facebook had 0 deep or machine learning patents in 2010, while just six years later, in 2016, this number shot up to 55. Facebook, now named Meta, utilizes artificial intelligence learning for features such as its custom news source algorithms, which show users news stories and posts that pertain to their needs and views.

At present, Meta has released its deep learning Segment Anything Model (SAM). SAM uses foundation models and zero-shot inference capabilities to segment objects in images with no prior training.

As more businesses recognize the significance of the technology, their profits and value surge. Bill Gates has been quoted to have said the following:

A breakthrough in machine learning is worth 10 Microsofts. Bill Gates

This further exemplifies the desirability of artificial intelligence in tech companies today.

What’s Next?

Deep learning is in action all around us in everyday life, from the personalized feed curated by Facebook in the morning, to the cars we drive home at night. It continues to fuel the innovation and expansion of tech companies.

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