Machine Learning Vs Deep Learning

Most people are unaware that Machine Learning was created in the 1950s. The first computer learning was created in 1959 by Arthur Samuel. Now fast-forward to today, when AI is not only cutting-edge technology but also a source of fascinating and well-paying employment. If you want to work in the field of artificial intelligence, machine learning, or deep learning, it is important to understand the differences between these terms. According to the Oxford Dictionaries, artificial intelligence, commonly called AI, is when a system can perform certain things that normally require human intelligence. This includes activities like seeing, hearing, speaking, making decisions, and translating between languages.

What is Machine Learning?

Machine learning is an application of artificial intelligence that makes use of algorithms that analyze data, gain knowledge from that knowledge, and then use that knowledge to make decisions.

By using Machine Learning, computer systems can now be programmed to understand and learn from given data inputs without having to be routinely reprogrammed. If we put it another way, they consistently improve their performances on a particular task—like playing a particular game—without further assistance from a human. Machine learning is used largely in a variety of industries, including finance, healthcare, art, and science..

Two popular methods of Machine Learning which are very commonly used are Machine learning with R and Machine Learning with python.

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What is Deep Learning?

Deep learning is a subset of machine learning where the computer learns in layers, similar to the way the human brain works. This makes it better at understanding and making intelligent decisions on its own.

Without even realizing it, you may already have encountered the outcomes of a comprehensive deep learning program! You have likely seen Netflix's suggestions for shows to stream if you've ever used the service. 

Additionally, some music streaming services such as Spotify also select tracks based on what you've already listened to or songs you've liked by clicking the "like" button or giving them a thumbs up.

These two functions both rely on deep learning. Deep learning is also used in Google's speech recognition and image recognition algorithms.

How does Machine Learning work?

The example of recognizing the image of a cat or dog can help explain how machine learning models function. The ML model uses input photos of both cats and dogs to determine this, extracting attributes like shape, height, nose, and eyes before applying the classification method and predicting the result.

Across a wide range of businesses, machine learning enables a variety of automated processes, from data security companies that look for malware to financial experts who want notifications for profitable transactions.

An on-demand streaming music service serves as a simple illustration of a machine learning algorithm. Machine learning algorithms help connect people's music preferences with those of other people who like the same kind of music. This way, the machine can figure out which new songs or artists to suggest to a listener. Many services that provide automated suggestions are using this method, which is frequently referred to as AI.

How does Deep Learning work?

We can understand how deep learning works using the same example of telling a dog from a cat. The deep learning model uses pictures as input and sends them straight to the algorithms. This means that there is no need for a human to do the job of extracting features from pictures. The several artificial neural network layers receive the images and predict the results.

The virtual assistants of online service providers, such as Alexa, Siri, and Cortana, employ deep learning to recognize your speech and the language you use in order to engage with them.

5 Key Differences between Machine Learning and Deep Learning

1.Human Intervention Unlike machine learning systems, which require a user to manually code the applied features based on the data type, a deep learning system seeks to learn such characteristics without additional human input (for example, pixel value, shape, and orientation).

2.Hardware - Deep learning systems need far more powerful hardware than machine learning systems do because of the volume of data handled and the complexity of the mathematical computations entailed in the algorithms used

3.Time - A deep learning system can take a long time to develop since it needs such large amounts of data, has so many parameters and uses intricate mathematical calculations. While machine learning can be finished in as little as a few seconds to as many hours, deep learning can take anywhere from a few hours to a few weeks.

4.Approach - Machine learning algorithms frequently divide data into parts, then integrate those parts to get a result or solution. Deep learning systems take a comprehensive approach to a problem or circumstance.

5.Applications - Basic machine learning applications include email spam detectors, algorithms that create evidence-based treatment plans for patients, and predictive programs (such as for predicting stock market price movements or the location and timing of the next hurricane). Deep learning has multiple usages. Cars that drive themselves use deep learning to do things like avoid obstacles and recognize traffic lights. You might also see it in services like Netflix and music streaming services. Deep learning is also used for facial recognition.

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Machine Learning VS Deep Learning – Comparison in a broader way 

1.Definition

  • Deep learning – A branch of Machine Learning which focuses on how well computers can simulate the human brain to solve extremely challenging AI challenges.
  • Machine Learning - A branch of Artificial Intelligence that focuses on how computers can learn rather than being programmed.

2.Data Feeding

  • Deep learning – It provides raw inputs to neural Networks. 
  • Machine Learning – It provides structured data that builds its model.

3.Volume of Data

  • Deep learning - Deep Learning models work with datasets that contain millions of data rows.
  • Machine Learning - ML models handle datasets with tens of thousands of rows of data.

4.Training Time

  • Deep learning – Because of massive data points, it ends up taking a large amount of time. 
  • Machine Learning – Because of comparatively small data, it takes lesser time. 

5.Human Involvement

  • Deep learning - Deep Learning models are challenging to construct because they make use of intricate hierarchical neural networks; they have the capacity to learn on their own.
  • Machine Learning - While building machine learning models is simple; improved projections require more human interaction.

6.Feature Engineering

  • Deep learning - Neural networks instantly identify crucial properties without the need for feature engineering.
  • Machine Learning – For ML, Feature engineering is a human-driven process.

7.Goal

  • Deep learning -   The purpose of Deep Learning is to simulate how the human brain thinks. 
  • Machine Learning - The purpose of Machine Learning is to produce an output that is as close as possible to the desired output.

8.Interpreting Results
  • Deep learning - The results of a complex, and multi-layered neural network are challenging to understand.
  • Machine Learning – It's fairly easy and simple to understand the outcome of the ML Model.

9.Performance

  • Deep learning - On large datasets, deep learning models outperform other models.
  • Machine Learning - When applied to small datasets & medium-sized datasets, ML models perform well.

10.Applications

  • Deep learning- Customer service, image processing, object & speech recognition, computer vision, NLP, and so forth.
  • Machine Learning - Pattern recognition, recommendation systems, and fraud detection are a few examples.

11.Outputs

  • Deep learning - It includes both numerical numbers and free-form components, such as text and sound.
  • Machine Learning - It is composed of numerical values, such as how scores are categorized.

12.Use of Algorithms

  • Deep learning - A neural network is used in deep learning to analyze input over several layers. These explain the characteristics of the current data and their connections.
  • Machine Learning - Many automatic algorithms are used in machine learning. These transform into numerous model functions that use data to forecast future behavior.

13.Detection and Depiction of Algorithms

  • Deep learning - When they are put into production, the deep learning algorithms essentially perform an analysis of their own data.
  • Machine Learning - The algorithms for assessing the specified variables available in data sets are found and examined by different data analysts.

14.Represented Data

  • Deep learning - Because deep learning uses ANN, the data that is represented in this scenario is different (neural networks).
  • Machine Learning - Due to the utilization of unstructured data and information in machine learning, the data that is represented in this situation is highly different.

15.Correlation

  • Deep learning - It belongs to the category of machine learning.
  • Machine Learning - It is the superset of the deep learning proces

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Limitations of Machine Learning 

Now let us see some limitations of Machine Learning that led to the development of Deep Learning. 

  • Feature engineering is the process of handling features in such a way that they result in good models. With machine learning, you have to tell your computer which ones are apples and oranges by giving it size information or color differences between them; but with neural networks in deep learning automatically picks and differentiate both fruit types without any human assistance required. In essence, feature engineering is deliberately carried out by humans in machine learning, whereas it is carried out automatically by the model in deep learning.
  • Complex AI problems like Natural Language Processing, Image Recognition, etc. cannot be solved by machine learning techniques.
  • Machine Learning does not perform to their best with larger datasets while Deep Learning can perform with both large as well as small data sets 

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 Conclusion 

We have learnt how Machine Learning and Deep Learning are different from each other. One must have a thorough understanding of both if they want to make a career in any of the two. 

That’s all for now! We will keep you updated on any new findings or breakthroughs in the world of machine learning and deep learning. In the meantime, if you have any questions or want to learn more about how these technologies can benefit your business, please don’t hesitate to get in touch. We would be happy to help!

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Saritha Reddy
Saritha Reddy
Research Analyst
A technical lead content writer in HKR Trainings with an expertise in delivering content on the market demanding technologies like Networking, Storage & Virtualization,Cyber Security & SIEM Tools, Server Administration, Operating System & Administration, IAM Tools, Cloud Computing, etc. She does a great job in creating wonderful content for the users and always keeps updated with the latest trends in the market. To know more information connect her on Linkedin, Twitter, and Facebook.

They aren't the same. Both are subfields of AI, and deep learning is a subset of machine learning, as we've already explained. Algorithms for machine learning only utilize structured data. Humans must carry out the feature engineering stage if the data is unstructured. On the other hand, deep learning can also be used to process unstructured data.

This is a difficult question to answer, as both methods have their own advantages and disadvantages. Machine learning is more efficient at handling large amounts of data, while deep learning can learn more complex patterns. Ultimately, it depends on the task at hand as to which method is more appropriate.

Without machine learning, it is difficult to learn deep learning. Since machine learning is a subset of deep learning, it is best to first become familiar with machine learning before advancing to deep learning. The technique of employing algorithms to separate data and generate predictions based on that data is known as machine learning. Machine learning techniques like deep learning employ algorithms to learn from data representations rather than the actual data. Deep learning is typically utilized for applications like natural language processing or picture recognition.

Yes, data scientists frequently utilize deep learning to develop models and make predictions. Data scientists can use deep learning to find hidden patterns and insights in large amounts of data. Deep learning can frequently outperform conventional machine learning methods. For these reasons, data scientists are using deep learning more frequently to tackle challenging issues.

Data scientists and machine learning engineers are at different stages of development within the same project or organization. Simply said, a data scientist will evaluate data and draw conclusions from it. A machine learning engineer will concentrate on creating software and delivering it.