Is deep learning the same as Machine Learning?

The development and rise of Artificial intelligence not only led to the global digital transformation of businesses, but the daily routines and habits of humans as well. Examples of AI involvement in our daily schedules and processing are predictive keyboards, emojis, virtual assistants on multiple devices, and face recognition technologies. Artificial Intelligence has 4 subsets developing at rocket speed and improving user experience in numerous businesses. They are:

1.Machine Learning –

The most applicable subset of AI. It includes algorithms that divide the data, learn from it, and use that knowledge in order to make trustworthy, data-related decisions.

2.Deep Learning

– Subfield of Machine Learning that arranges the algorithms in layers in order to create artificial Neural Networks that can independently learn and make decisions.

3.Computer Vision

– Also a subfield of AI, whose main task is to recognize and derive correct and valuable information from digital images.

4.Natural Language Processing

– Branch of AI, which helps computers understand and correctly context  of both spoken words and written text.

So if they are both subsets of AI, how does Deep Learning differ from Machine Learning?

The main difference between Machine Learning vs Deep Learning is in the way they perform. Machine Learning models thrive to get better with every new dataset incorporated, they are dependent on human work. If the model algorithm returns questionable predictions, the Data Scientists/Engineers must intervene and make some adjustments. In Deep Learning, an algorithm can evaluate a prediction accuracy simply through its own Neural Network, without the need for human intervention. Here are a few more differences between Deep Learning  vs Machine Learning such as:

Testing. CI/CD. Monitoring.

Because ML systems are more fragile than you think. All based on our open-source core.

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