Deep learning vs. Machine learning – What’s the difference? Lets understand the similarities and differences between the same.
From science fiction to reality, Artificial Intelligence has come a long way in its digital evolution. Today’s breakneck AI pace looks unstoppable, as tech evangelists are integrating it with multiple solutions and services.
Machine learning, a subset of AI, and deep learning, a subset of machine learning, is gaining traction among technologists. However, even as their popularity grows, many organizations are still unaware of their differences.
- What learning approaches do they follow?
- What requires more computational power?
- Which promotes faster execution Machine Learning or Deep Learning?
- Deep Learning vs. Machine Learning, which is better for their organization?
With AI being the tech “buzzword”, it is essential to know more about and the differences between deep learning and machine learning.
Deep learning vs. machine learning – What’s the difference?
What learning approaches do they follow?
Machine learning utilizes structured data to feed an algorithm. It learns through it and generates results. Some common ML uses are traffic alerts and virtual assistant automation.
Deep learning uses neural networks to process unstructured data. These neutral networks use multiple layers to learn and can mimic the way the human brain’s neurons act and react. When compared to machine learning, deep learning uses fewer algorithms. Two notable examples of deep learning are facial recognition and autonomous cars.
Machine Learning or Deep Learning – What requires more computational power?
Since machine learning processes small and medium data sets, organizations can run ML on machines with less computing power.
Deep learning neural networks involve multiple parameters and complex mathematical formulas. As such, they require higher computational power and need more powerful hardware. The most commonly used machines for deep learning are graphical processing units. Since deep learning requires more computational power it tends to be more expensive to implement than machine learning.
Is Human Intervention Required?
Machine learning can process only structured data, and therefore requires human intervention to hand-code the data for learning. In contrast, deep learning requires no human intervention for learning processes.
For instance, to identify a book and pen, coders must explain both the book and the pen’s characteristics and label it in code for ML to learn. Through neural networks, deep learning can seamlessly learn and identify without human intervention.
Which promotes faster execution – Machine Learning or Deep Learning?
Because it uses small and medium data sets, ML takes only a few minutes to hours to process the given structured data.
With a tremendous amount of data sets to process, deep learning uses multiple layers to learn using deep neural networks. This involves a myriad of parameters and complex mathematical formulas. Deep learning often will take hours or weeks to process data. Training data sets are usually very large for deep learning.
Deep Learning vs. Machine Learning. Which is better?
There is no definite answer. Each has their roles to play. There is no doubt these forms of AI are the future. AI overall still has hidden potential to unleash. Organizations should keep a close watch on emerging trends to discover what else AI can do.
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