Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are different algorithms (e.g. neural networks) that help to solve problems.
Deep learning, or deep neural learning , is a subset of machine learning, which uses the neural networks to analyze different factors with a structure that is similar to the human neural system.
ref:https://www.analyticsvidhya.com/blog/2017/04/comparison-between-deep-learning-machine-learning/
ref:https://medium.com/ai-in-plain-english/artificial-intelligence-vs-machine-learning-vs-deep-learning-whats-the-difference-dccce18efe7f
Deep learning (DL) was born as a sub field of machine learning. This was inspired by the advent of even more powerful computers and much quicker ways to access larger volumes of data.
This indicates that the major difference between deep learning and machine learning is that deep learning is all about scaling up the capabilities of machine learning models. The objective of this sub field is to effectively work around the overfitting issues of other machine learning methods. So, by using larger artificial neural networks and more capable computers for training these models with larger quantities of data, the performance of these networks is known to continue to increase over time.
Deep Learning is, after all, a form of Machine Learning.
Similarly, Corvette has become such a popular luxury car that many people forget it’s a Chevy at the end of the day.
Deep Learning is designed (or at least is the default choice) for particular jobs that no other Machine Learning tools can solve, to cut a long story short. As a result, extra attention has been paid to it.
Computer Vision, Natural Language Processing, Speech Recognition, and more industries use deep learning. There is one component that all of these fields have in common: they are driven more by intuition than logic.
As an example,
PReLU-Net (a particular form of Deep Learning architecture) is the first neural network to achieve human-level accuracy in 2019. This shows that humans are intrinsically more efficient at solving these issues.
Because it is affected by human brain networks, Deep Learning surpasses other machine learning algorithms. Thankfully, machines fail the Turing test because they cannot form intuition.
The critical distinction between the human brain and Deep Learning is processing information.
We take a top-down strategy, determining if a particular image is of a cat or dog before looking for any of the lower-level traits.
We’d focus on the lower-level objects after asking for further information.
On the other hand, Deep Learning architectures create lower-level features first, then integrate them to build the next level of components in a cascade pattern. This is a bottom-up strategy.