Deep Learning VS Machine Learning: Unleashing Astounding Insights

The advancement in the realm of artificial intelligence is huge. And! Deep Learning & Machine Learning are armors that are coveted for their inexplicable potential in developing software applications. 

Actually, these two are major terms of AI with an interchangeable hype around them. For businesses, deep learning and machine learning acquire significant importance due to their potential of learning from data. 

Though, many eyes wonder about their importance in the market and current scenario. Surely, you would be having several questions in your head regarding Deep learning and machine learning. Thus, in this blog, we would have an investigative insight into Deep Learning VS Machine Learning. 

So, stay tuned with me. 

But!!! Before diving into deep learning vs. machine learning, let's take a glance at some notable statistics. 

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•  By 2025, the estimated size of the US deep learning software market will be $80 million. 

• By 2027, the global deep learning market is projected to surpass $44.3 billion at the rate of 39.2% CAGR. 

• In the first quarter of 2019, $28. 5 billion funding has been allocated to ML. That makes it a top AI investment stream. 

• The expected global value of the ML market is $117.19 billion by 2027, which is increasing at the rate of 39.2% CAGR.  

• Per research by McKinsey, 50% of respondents said that their companies had embraced AI in a business function. 

The impact of machine learning on users: 

• ML-based voice-activated search accounted for 3.52 billion, which is almost half of the world population. 

• During COVID-19, Global voice assistance grew at the rate of 7%. 

• 1/3 of IT leaders are eager and planning to leverage machine learning for business analytics. 

• 25% of Businesses are planning to embrace ML for security purposes. 

• 16% of IT leaders are planning to use ML in sales & marketing. 

SO, from the above statistics, you would have understood how deep learning and machine learning services are deepening their roots in the market and influencing businesses and people. Let's learn about the basic concept of these terms. 

What is machine learning?

What do you relate to the term machine learning? Is it "learning of a machine" or "a machine that can learn"?

Well! The term signifies something similar but not complete. Actually, ML is the use of AT to provide the system the ability to "Learn" automatically without any human intervention. It also includes the process of improving the human experience with machines without being explicitly programmed.

ML's main focus is to develop a computer program that can learn and access data themselves. For which, ML algorithms are created that can observe data, find patterns or instruction for better decision-making. These algorithms are designed in a way that they can adjust a computer program's actions without human intervention.

In this, the use of semantic analysis is also significant, and they enable algorithms to mimic the human ability and understand the meaning of text-based information. ML algorithms are categorized into two: supervised machine learning, Unsupervised Machine learning.

 

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Here are some other types of ML algorithm: 

• Semi-supervised machine learning algorithms  

• Reinforcement machine learning algorithms

ML allows analysis of humongous data to deliver faster, accurate results in order to identify profitable opportunities or dangerous risks.  

 

What Is Deep Learning?

The term deep learning already signifies that diving deeper into learning. Actually, it is a subset of machine learning that includes AI functioning for imitating the human brain in data processing and pattern creation. Its major application is better decision-making by learning deeply from given data, whether visual or text. 

DL includes network capabilities of learning from data that is unlabeled and unstructured. It is also called deep neural learning or deep neural network. Moreover, the Deep learning technique offers an enhanced ability to find and amplify small patterns. In this process, a number of computational nodes work together to munch through data and provide a final result as the prediction. 

Deep learning can learn from data without human aid and draw data in both unstructured and unlabeled formats. 

Deep learning can detect fraud and money laundering by munching financial data and functions. 

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From this explanation, you would have understood that both machine learning and deep learning. Let's go dive into the comparison of Deep learning VS Machine Learning. 

Deep Learning Vs. Machine Learning: How Do They Work?

How Does Machine Learning Work? 

Machine learning algorithms work simply by exploring and identifying a pattern in data and include very small human intervention. It teaches computers to think the way a human does. Moreover, it includes a set of rules and data-defined patterns that automate the data processing. 

ML algorithm allows companies to transform human intervention processes into automated ones by using computer vision, deep learning, and AI algorithms. They majorly include unsupervised and supervised data processing for forecasting and data prediction. 

How Does Deep Learning Work?

Deep Learning is basically an ML method that allows you to train AI to predict the final result/output for the provided inputs. Let's learn how deep learning works by example. 

In a price estimation service, there will be an input of products, their name, and their quantity. We will know the final cost bypassing the data from the neural network. 

In this process, neurons are grouped in three different layers: 

• Input Layer
• Hidden layer (s)
• Output Layer

The input layer will receive the data inputs, which would be: name of the product, the quantity of product, buying price, etc. This first layer would pass the inputs to the first hidden layer. 

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The next is the hidden layer that performs mathematical computation on inputs. Though, the concern is making neural networks of data and deciding on numbers of hidden layers. Here comes the use of "Deep" learning, which is about having more than one hidden layer.

Deep learning processes that data by sorting the nodes on the basis of weight and refining information. 

While the output layer provides the final prediction on given data. 

So, this is how deep learning works. Though, it has been evolved hand-in-hand with the expansion of tech. The digital era has brought huge data containing all forms and every region of the world, referred to as Big Data, on which deep learning algorithms work to refine and predict information. 

It is clear that deep learning is part of machine learning but very different in approach while it comes to the usage of data. Let's glean more insight into Machine Learning Vs. Deep Learning. 

Moreover, To leverage the potential of these aspects, hire machine learning engineers deft and expert in AI/ML integration. 

 

Machine Learning VS Deep Learning: Comparison 

Let's learn about the major difference by considering an example for deep learning Vs. Machine learning.

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Here you can see a collection of images of dogs and cats. Now we need to apply machine learning and deep learning networks to understand the work. 

See the image above. What you will see is a collection of pictures of cats and dogs. Now, suppose that you need to identify images of dogs and cats separately through the usage of ML and DL networks.

How can this problem be solved with machine learning?

To help ML algorithms classify images in a collection according to both dog and cat categories, you will need to present these images simultaneously. But how would the algorithm know which one is dog or cat?

The answer to this question, as in the above definition of machine learning for divers, is structured data. You only label pictures of dogs and cats in a way that defines the specific characteristics of both animals. This data is sufficient to learn machine learning algorithms, and they will then continue to work based on the labels they understood and classify the millions and pictures of both animals according to the characteristics learned by those labels.

How would DL solve the problem?

Deep learning networks will adopt a different way to solve this problem. The main advantage of the Deep Learning Network is that it does not require structured/labeled data of images to classify both animals. Artificial neural networks that use deep learning send inputs (image data) through different layers of the network, and each hierarchical network defines specific characteristics of images, how it works to solve the problems of our human brain similarly — by asking questions through different hierarchies of concepts and related questions to find an answer.

After processing data through layers within a deep neural network, the system finds an appropriate identifier to classify two animals from its images.

Surely, now you would have understood how both are different in approach and problem-solving. 

Machine learning labels the unstructured data for identifying patterns while deep learning works on the layer of the network and creates a hierarchy of networks to identify characteristics of data. 

Deep Learning Vs. Machine Learning: When You Can Use Them?

The biggest concern for both developers and organizations is the usage of ML and DL. Aren't you wondering when you can leverage these tech concepts? Let's gain some insight into it. 

When to use deep learning?

You can leverage deep learning:

• If you are a company with humongous data to derive interpretation from. 
• If you need to solve too many complex problems related to machine learning. 
• If you are able to spend a lot of computational resources to acquire hardware and software for deep learning network training. 

When to use machine learning?

You can exploit machine learning algorithms in various instances such as:

• To train machine learning algorithms via your structured organizational data. 
• To leave the competition behind in your industry and leverage AI to enhance business growth. 
• To automate your organizational operations such as identity verification, marketing, advertising, data gathering, and leverage rising opportunities for the future. 

Deep Learning Vs. Machine Learning: A Quick Glance At Major Differences

Deep Learning: 

• Processes Unstructured/Unlabeled Form Of Data 

• Rely On Network Layers Of ANN 

• DL Networks Completely Avoid Human Intervention & Cater Accurate Results

• DL Networks Learn From Their Own Mistakes 

Machine Learning:

• Processes Structured /Labeled Data 

• No Layers For Data Processing 

• ML Algorithms Require Them To Correct By Human Intervention 

• ML Required To Be Trained With Human Aid. 

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Wrapping UP 

Machine learning and Deep learning both have their own play field. These tech streams can turn out very useful in catering to people's automated and more futuristic interaction with devices and brands.

While ML can be trained from data, DL can help in sorting unstructured data to find risks, glitches and predict in various instances; If you want to leverage these technologies, then you can connect with a machine learning application development company and can find out all potential application of this tech stream for your brand.

Moreover, I hope this article will solve all your queries regarding deep learning Vs. machine learning. If you want to know more, let me know in the comments.