Deep learning

Difference between deep learning vs. machine learning?


It cannot be easy to understand the most recent advances in artificial intelligence (AI). However, suppose you are just interested in learning the basics. In that case, you can reduce many AI innovations down into two concepts: deep learning and machine learning. It can seem that these terms are interchangeable buzzwords. Therefore, it is crucial to understand the differences.

What are these dominant concepts in the conversation about artificial intelligence? And how are they different?

What is deep learning?

Deep learning algorithms can be described as both a complex and sophisticated evolution of machine-learning algorithms. This field is receiving a lot of attention recently, and for good reasons: Recent advances have resulted in previously impossible results.

Deep learning is a method of analyzing data using a logic structure similar to what a human would use to conclude.

This ANN was inspired by the biological neural networks of the human brain. It allows for a learning process that is far more powerful than standard machine learning models.

Deep learning is being used in many areas today. Deep learning is used in automated driving to detect objects such as pedestrians or STOP signs. 

Deep learning is also a big part of the consumer electronics market. Deep learning algorithms are used to recognize your preferences and respond to your voice in-home assistance devices like Amazon Alexa.

What is machine learning?

Machine learning is an AI application that uses algorithms to analyze data, learn from it, and then use what they have learned to make informed decisions.

A streaming music service that allows you to stream on-demand is an example of a machine-learning algorithm. Machine learning algorithms match listeners with similar musical tastes to help the service decide which songs or artists it should recommend. 

It is commonly called artificial intelligence (AI) and is used in many automated recommendation services.

Machine learning is used to automate many tasks across many industries. It includes data security firms that search for malware and finance professionals who need alerts about favorable trades. 

Machine learning is a complex mix of math and code that serves a mechanical function in the same way as a flashlight, car, or computer screen. It can learn from data and get better with time. 

It’s similar to a flashlight that would turn on when you say “it’s darkness” and recognize different phrases that contain the word “dark.”

Deep learning and deep neural networks are two of the most exciting ways machines can learn new tricks.

Five key differences between deep and machine learning

There are many differences among these subsets, but here are five that are most important.

1. Time

Although machine learning systems are easy to set up and use, their power may be limited. While deep learning systems can take longer to set up, they can produce results instantly (although their quality will likely improve as more data is available).

2. Hardware

Deep learning algorithms are more complex than machine learning programs. Machine learning programs can be run on most computers. However, deep learning requires far more resources and hardware. This increased power demand has led to an increase in the use of graphics processing units.

The GPU is helpful because of its high bandwidth memory and the ability to hide latency in-memory transfer (delays) due to thread parallelism (the ability for many operations to run efficiently simultaneously).

3. Applications

Machine learning is already being used in your bank, email, and doctor’s offices. Deep learning technology allows for more complex and autonomous programs like self-driving cars or robots that can perform advanced surgeries.

4. Human Intervention

Machine learning dependent on human intervention to achieve results. Deep learning is more challenging to set up but only requires minimal intervention.

5. Approach

Machine learning requires structured data, and traditional algorithms such as linear regression are used. Deep learning uses neural networks and can handle large amounts of unstructured data.

Machine learning and deep learning: The future

Deep learning and machine learning will have a profound impact on our lives for many generations. They will transform virtually every industry. Machine involvement could replace dangerous jobs such as space travel and work in harsh environments.

People will also turn to artificial intelligence for rich entertainment experiences that look like science fiction.

Machine learning vs. deep learning: Customer service?

Machine learning algorithms are used in many of the AI applications that customer service uses today. They are used to improve agent productivity and reliability, as well as drive self-service.

These algorithms are fed data from the constant flow of customer queries. It includes relevant context about the customers’ issues. This context is then incorporated into an AI application to make predictions that are faster and more accurate. 

Artificial intelligence has become a promising prospect for many businesses. Industry leaders speculate that customer service will be the most useful application of AI-related business.

As deep learning improves, we will see more advanced applications of AI in customer service. Zendesk’s Answer Bot is a great example. It uses deep learning to understand the context and determine which help articles it should recommend to customers.

Machine learning and deep learning are great career options

To help machine learning and deep learning achieve their highest potential, it will require the ongoing efforts of skilled individuals. Several critical career paths have attracted top talent, although each field has its own unique needs.

Machine Learning Engineers

Machine Learning Engineers integrate the models of data scientists into complex technological and data systems.

They also oversee the programming and implementation of automated controls or robots that act based on incoming data.

It is crucial work. The vast volume of data and computing power required for this task requires high expertise and efficiency to be cost- and resource-efficient.

  • The average Glassdoor salary is $114k/year
  • Average ZipRecruiter Salary: $131k/year

Computer Vision Specialist

Computer Vision Specialists are computer vision specialists who help computers to make sense of 2D and 3D images. They are crucial for many practical applications of deep learning, such as the augmented or virtual reality spaces.

It is only one example of a career in the machine learning ecosystem. Every industry will have its specialist to help combine the power of artificial intelligence and industry goals.

  • The average Glassdoor salary is $114k/year
  • Average ZipRecruiter salary is $96k/year

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