Machine Learning vs. Deep Learning: What's the Difference?

Both the terms " Machine learning" and "Deep learning" are often used interchangeably. Yet, in case you're thinking about a career in machine learning and AI, it's critical to know how both are different from each other. The most straightforward takeaway for understanding the difference between machine learning and deep learning is to realize that deep learning is machine learning. Like machine learning is a type of AI, deep learning is also a type of machine learning or subset of machine learning.

In simple words, deep learning is an evolution or upgraded version of machine learning. It utilizes a programmable neural network that empowers machines to make correct choices without assistance from people.

Let’s understand Machine learning and Deep learning one by one:

What is machine learning?

Machine learning is a subset of Artificial Intelligence (AI) that offers the capacity to learn and improve for a task without being programmed to that level. Machine learning uses the information to find precise outcomes. Machine learning algorithm focuses on developing a computer program that accesses the information and utilizes it to learn from it. A simple example of a machine learning algorithm is an on-demand music application.

What is Deep learning?

Deep Learning is a subset of Machine Learning where the artificial neural organization and repetitive neural network connect. The algorithms are created precisely much the same as AI. However, it comprises multiple levels of calculations. Every one of these algorithm networks is together called the artificial neural network. Some of the applications of Deep learning are Virtual assistants, face recognition, and more.

Comparing Machine Learning and Deep Learning:

Now you are aware of both the terms and their meaning. Now we will be comparing Machine learning and deep learning based on several parameters.

Dependency of data

The main difference between deep learning and machine learning is when it comes to performance as the size of data increases. At the point when the data is little, deep learning algorithms don't play out that well. It is because deep learning algorithms need a lot of information to understand them impeccably. Then again, conventional machine learning algorithms with their high-quality principles win in this situation.

Dependencies on hardware

Deep learning algorithms vigorously rely upon high-end machines instead of traditional machine learning algorithms to deal with low-end machines. The necessities of deep learning algorithms incorporate GPUs, which are a vital part of its working. Deep learning algorithms innately do a lot of matrix multiplication tasks.

Critical thinking approach

When solving an issue utilizing machine learning algorithms, it is prescribed to break the issue into various parts, settle them independently, and join them to get the outcome. Deep learning interestingly advocates taking care of the problem from start to finish.

Execution time

Usually, a deep learning algorithm takes a long time to prepare. There are such countless parameters in a deep learning algorithm that preparation takes longer than expected. Best in class deep learning algorithm ResNet requires around fourteen days to prepare totally from scratch. Though machine learning substantially takes less time to prepare, going from a couple of moments to a couple of hours.


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