Why is Deep Learning Called Deep? [Complete Deep Learning Definition]

Why Is Deep Learning Called Deep? You must have asked that question. In this post, I will attempt to answer that question and give a deep learning definition.

I remember Deep learning making the news when Google’s AlphaGo algorithm defeated Lee Sedol, one of the world’s top Go players. Google has made significant investments in deep learning, and AlphaGo is only the most recent deep learning effort to garner headlines.

Deep learning is significantly used in Google’s search engine, speech recognition system, and self-driving automobiles. They employed deep learning networks to create a computer that selects an appealing still from a YouTube video to serve as a thumbnail. 

Google Smart Reply, a deep learning network that produces brief email messages for you, was launched late last year. Deep learning is undeniably powerful, but it can also be perplexing. What exactly is deep learning, and how can it help you if you’re not Google?

Before we move further let’s first give a deep learning definition.

What Is Deep Learning? [Deep Learning Definition]

Deep Learning is a subclass of Machine Learning that learns to represent the world as a layered hierarchy of concepts. Each process is defined as more straightforward ideas and more abstract representations calculated in less abstract ones.

A deep learning methodology learns categories sequentially through its hidden layer architecture, first establishing low-level categories like letters, then slightly higher level categories like words, and higher-level categories like sentences. 

In picture recognition, this entails recognizing light/dark regions before classifying lines and forms to enable face identification. Each neuron or node in the network represents a different component of the whole, and when combined, they offer a complete representation of the image. 

Each node of the hidden layer is assigned a weight that symbolizes the associative strength with the output. Those weights are changed and updated, usually through backpropagation using optimization functions (e.g. gradient descent).

This is as good as a deep learning definition goes.

Why Is Deep Learning Called Deep?

Deep Learning is called Deep because of the number of additional “Layers” we add to learn from the data. If you do not know it already, when a deep learning model is learning, it is simply updating the weights through an optimization function. A Layer is an intermediate row of so-called “Neurons”. The more layer you add to your model, the deeper you go hence the name “deep” learning”.

Let’s take as an example the way Artificial Neural Networks (ANN) are structured. Five decades ago, neural networks were just two layers deep because massive networks were not computationally viable. The concept of deep learning did exist back then, but we did not have hardware powerful enough to be able to make use of if. It is now often to have neural networks of 10+ layers and 100+ layer ANNs tested. Now computers can go deeper by adding more and more layers. Consequently, the term deep learning became popular in the past few years.

Computers can now observe, understand, and react to complicated events or better than humans, thanks to deep learning’s use of several levels of neural networks.

Typically, data scientists devote a significant amount of effort to data preparation, such as feature extraction or identifying variables that are truly valuable for predictive analytics. Deep learning automates some of those tasks, making life easier.

Deep Learning Applications

To encourage this progress, numerous technological firms have made their deep learning libraries open-source, such as Google’s Tensorflow and Facebook’s open-source Torch modules.

As a result, there are many instances of deep learning circulating today, including:

• Google Translate uses deep learning and picture recognition to translate not just spoken but also written languages.
• DCGAN is used to improve and complete the appearance of human faces.
• Amazon, Netflix, and Spotify use deep learning recommendation systems to find the best deals, movies, and music.
• Take a photograph of anything, or the CamFind app will tell you what it is using mobile visual search technology. It produces quick, accurate results with no typing required. Take a photo to discover more. 
• Deep learning is used by all digital assistants, including Siri, Cortana, Alexa, and Google Now, for natural language processing and speech recognition.
• DeepMind’s WaveNet can create a speech that sounds more natural than the best existing Text-to-Speech systems. 
• Google PlaNet can analyze the photo and determine the photo’s location.
• DeepStereo: Converts Street View photos into a 3D space that displays previously unseen views from various angles by calculating each pixel’s depth and colour.
• Paypal is employing deep learning to detect payment fraud.

Deep learning has already assisted picture categorization, language translation, and speech recognition and now can tackle any pattern recognition challenge, all without the need for human participation.

Undoubtedly, revolutionary digital technology is used by an increasing number of businesses to develop new business models.

How Deep Learning Works?

Deep learning neural networks, also known as artificial neural networks, try to simulate the human brain using a mixture of data inputs, weights, and biases. These aspects collaborate to detect, categorize, and characterize things in data effectively.

Deep neural networks are made up of numerous layers of interconnected nodes, with each layer building on the preceding one to refine and maximize predictability. Forward propagation refers to the movement of calculations via a network. 

The visible layers of a deep neural network are the input and output layers. The deep learning model absorbs data for processing in the input layer, and the final prediction or classification performs in the output layer.

Backpropagation is another approach that employs techniques such as gradient descent to quantify errors in predictions and then modifies the weights and biases of the function by travelling backwards through the layers to train the model. 

Forward and backpropagation operate together to allow a neural network to make predictions and correct faults. The algorithm evolved.

In the most basic terms, the above describes the most basic sort of deep neural network. On the other hand, deep learning techniques are highly sophisticated, and many types of neural networks exist to solve specific issues or datasets. As an example,

• Convolutional neural networks (CNNs), which are regularly used in computer vision and image classification applications, can recognize characteristics and patterns within an image, allowing tasks such as object detection and recognition to perform.
• Because they utilize sequentially or time-series data, recurrent neural networks (RNNs) are commonly utilized in natural language and speech recognition applications.

Why Is Deep Learning Important?

Deep learning has received a lot of interest because it excels at learning that has the stamina to be effective in real-world applications. For example, an ML training approach where the algorithm used all images to train labels with the item’s name in the picture. 

Each iterative step in testing and improving the model entails comparing the label on an image to the label supplied to the picture by the program to assess whether the program categorized the picture perfectly. This type of training is known as supervised learning.

Supervised learning is reasonably quick and requires fewer computer resources than some other machine learning training approaches. However, it has a significant disadvantage in real-world applications.

Every day, social media, hardware, software service agreements, app permissions, and website cookies collect massive amounts of information about people. This data has the potential to be beneficial to businesses at all levels. 

The issue is that all of this data is unlabeled and cannot use to train supervised learning-based machine learning applications. The data must label by hand, which is a time-consuming and costly operation.

Deep learning networks are immune to this disadvantage since they excel at unsupervised learning. The primary distinction between supervised and unsupervised learning is that unsupervised learning does not label the data. Even if the images of cats do not have the label “cat,” deep learning networks will learn to recognize the cats.

For those interested in real-world applications, the capacity to learn from unlabeled or unstructured data is a huge advantage. Deep learning opens the door to a rich mine of unstructured data for anyone with the imagination to exploit.

What Are The Differences Between Machine Learning And Deep Learning?

1. Human Intervention

Unlike machine learning systems, which need a person to select and hand-code the applied characteristics depending on the data type, deep learning systems attempt to learn those characteristics without extra human interaction. 

Consider a face recognition program. The algorithm initially learns to identify and recognize face borders and lines, more critical aspects of faces, and eventually entire face representations. The quantity of data involved in accomplishing this is massive, and as time passes and the algorithm trains itself, the likelihood of best replies grows.

2. Hardware

Deep learning systems demand substantially more powerful hardware than ordinary machine learning systems due to the volume of data handled and the complexity of the mathematical calculations involved in the algorithms utilized. 

Graphical processing units are one sort of deep learning hardware (GPUs). Machine learning programs execute on computers with less computational capability.

3. Time

As you might think, a deep learning system can take a long time to train due to the large data sets required and the numerous parameters and intricate mathematical formulae involved. 

Deep learning can take anything a few seconds to a few weeks, whereas machine learning may take anywhere from a few seconds to a few hours!

4. Approach

Machine learning algorithms often split the input into components, which integrate to provide a result or solution. Deep learning methods examine a whole problem or situation in a single pass. 

For example, if you wanted the software to recognize certain items in an image, you’d have to go through two processes with machine learning: first object detection, then object recognition.

In contrast, with the deep learning software, you would enter the image, and after training, the software would provide both the detected items and their position in the picture in a single output.

5. Applications

You’ve undoubtedly deduced that machine learning and deep learning systems use for different purposes. They are used in the following places: Predictive algorithms, email spam IDs, and algorithms that develop evidence-based treatment plans for medical patients are examples of basic machine learning applications.

When To Use Deep Learning Or Not Over Other Approaches?

1. If the data amount is large, Deep Learning outperforms conventional approaches. Traditional Machine Learning techniques, on the other hand, are preferred when dealing with tiny amounts of data.
2. Deep Learning approaches require high-end infrastructure to train in a reasonable amount of time.
3. When there is a shortage of domain awareness for feature introspection, Deep Learning approaches outperform others since you have to worry about tasks less.
4. Deep Learning excels at complicated issues like image classification, natural language processing, and speech recognition.

What Are The Advantages Of Using Deep Learning?

You may be wondering why a large number of IT beasts are slowly adopting deep learning. To understand why you must consider the benefits of utilizing a deep learning methodology. Here are five critical benefits of using this technology.

1- Maximum Utilization Of Unstructured Data:

According to research, a large amount of an organization’s data is unstructured since its bulk resides in various formats such as images, texts, and so on. 

Unstructured data is difficult to examine for the stock of machine learning algorithms; therefore, it goes unused, where deep learning comes in handy.

You can train deep learning algorithms using various data types while still obtaining insights relevant to the training goal. 

For example, you may utilize deep learning algorithms to find any current relationships between industry analyses, social media activity, and other factors. It is done to forecast a different organization’s stock price in the future.

2- Elimination Of The Need For Feature Engineering

Feature engineering is a critical task in machine learning since it improves accuracy, and the process might often necessitate domain expertise about a specific problem. One of the most significant advantages of employing a deep learning methodology is its capacity to do feature engineering. 

3- Ability To Deliver High-Quality Results

Humans feel hungry or tired, and they occasionally make foolish blunders. It is not the case when it comes to neural networks. 

Once correctly trained, a deep learning model can execute thousands of regular, repetitive activities in a fraction of the time it would take a human. Furthermore, unless the training data is corrupted, the work’s quality never deteriorates.

4- Elimination Of Unnecessary Costs

Recalls are costly, and in some sectors, a recall may cost a company millions of dollars in direct expenditures. 

Personal faults that are difficult to train, such as tiny product labelling problems, can be recognized using deep learning. Deep learning models can also find problems that would otherwise be difficult to notice.

5- Elimination Of The Need For Data Labeling

Data labelling may be a costly and time-consuming task. Indeed, well-labelled data becomes unnecessary with a deep learning method since the algorithms excel at learning without any guidelines. Other ways to machine learning are not nearly as successful as this form of learning.

Final Words on the Deep Learning Definition

In this post, we have looked at a deep learning definition before talking about its importance and application. If you have another deep learning definition, please add it in the comment section below. Additionally, if I have missed on point on my deep learning definition, do let me know too.

Regarding the critical and other benefits of utilizing a deep learning methodology, it is reasonable to expect to see deep learning used on various high-end technologies such as in the future, Advanced System Architecture or the Internet of Things (IoT).

More meaningful contributions to the best commercial world of linked and intelligent goods and services might be expected.

Deep learning has progressed from a fad to an essential technology progressively implemented by a wide range of enterprises across numerous industries.

If you made this far in the article, thank you very much.

I hope this deep learning definition was of use to you. 

Feel free to use any information from this page. I’d appreciate it if you can simply link to this article as the source. If you have any additional questions, you can reach out to malick@malicksarr.com  or message me on Twitter. If you want more content like this, join my email list to receive the latest articles. I promise I do not spam. 

[boldgrid_component type=”wp_mc4wp_form_widget”]