Machine Learning vs Deep Learning

Usually, people confuse machine learning with deep learning or vice-versa, though both are under the same domain. Deep Learning is the sub-domain of Machine Learning having a broader domain as Artificial Intelligence.

In today's technological advancement, Artificial Intelligence is leveraging market and sales highly. People in the corporate world are trying to find applications of Artificial Intelligence in their businesses. Both machine learning and deep learning are useful in delivering AI solutions for businesses. Basically, be it machine learning or deep learning it is about making a model learn and run it based on some test and perform training to find out the end results. Moving on let us find out the differences between the two technological giants.

The differences between the two are:

Concept-Based

Machine Learning: It uses data to learn from it and apply that information to make decisions. For example, a weather forecasting agency decides whether it would be sunny, cloudy, or clear for a particular day. Based on the weather conditions and environmental changes, the decision by weather forecasting comes before the world.

Deep Learning: In contrast with machine learning, it uses structures and algorithms to generate required results or decisions. It is a sub-domain of machine learning and also the other name for artificial neural networks.

With artificial neural networks, it has become easy to detect human activity, expression detection, etc.

Application-Based

Machine Learning: Machine Learning applications differ from deep learning applications. If you wish to implement simple machine learning algorithms, you can work with prediction analysis, email spam identification, cancer or tumor detection problems, etc.

Other applications include classification models like Naval Mine Identifier (NMI). In this, a machine learning model identifies whether the obstacle is mine or rock.

Deep Learning: Although it is a sub-domain, it applies to high order human intervention problems like self-driven cars, where neural networks are involved. It solves problems that require multi-layers to build a model, and neural networks come into practice.

Apart from this, facial recognition is another application of deep learning. It uses many layers of neural networks to identify minute detail on the human face. There are different libraries like OpenCV that solve many deep learning problems.

Have you ever thought of editing your image with the help of code? If not, you should know OpenCV can do this for you. A bilateral filter and OpenCV can help you give a cartoon look to your image.

It is the power of deep learning with python.

Hardware-Based

Machine Learning: Machine Learning can work on Central Processing Units and does not solely depend on complex hardware like Graphical Processing Unit (GPU). It can run on lower-end machines without much complication.

Deep Learning: In deep learning, the professionals deal with huge chunks of data so complex hardware, including Graphical Processing Unit (GPU) and Tensor Processing Unit (TPU) to process data.

Time Consumption

Machine Learning: Machine learning programs and solutions range from few seconds to few hours. Since machine learning problems require less complexity, it takes minimal time as compared to deep learning.

Deep Learning: Now you know that Deep Learning deals with complex datasets. Its processing time ranges from few hours to few weeks. It involves hard calculations and complex formulae to solve problems, so, more time taking than machine learning.

Final Thoughts

Machine Learning and Deep Learning are the technologies of Artificial Intelligence that helps in automation tasks. There are several fields already using these technologies, starting from weather prediction to self-driven cars.

Soon the world will come under the umbrella of Artificial Intelligence. Machine Learning employees data and generate decisions based on that, whereas deep learning involves structures and algorithms to generate results.