https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Deep Learning is a subset of Machine Learning algorithms.

It typically consists of technical neural networks and is characterized by using (many) multiple cascading (and possibly recurrent) processing layers.

Before the advent of cheap GPU ressources, technical neural networks tended to be relatively shallow, in terms of processing layers, due to the required processing power. Today this is no longer such a limiting factor, making deeper network architectures feasible. Hence the name deep learning.

For further reading look into Mr. Islams link to the nvidia blog-post or simply have a look at the Wikipedia-entry: https://en.wikipedia.org/wiki/Deep_learning

Conference Paper Tutorial-1: Machine learning and deep learning

I wish you good luck.

Deep learning is a fancy name for multilayer/stacked neural networks (and a growing set of related algorithms) to ease grant proposals getting approved.

Dear Maged

Your answer is simply depicted by this image (ImageRef: https://medium.com/@diamond_io/artificial-intelligence-101-everything-you-need-to-know-to-understand-ai-8e20fe4bd750)

Dear Maged Al-Quraishi, here are some differences that I have reviewed

• The main difference between deep learning and machine learning is due to the way data is presented in the system. Machine learning algorithms almost always require structured data, while deep learning networks rely on layers of ANN (artificial neural networks).
• Machine learning algorithms are designed to “learn” to act by understanding labelled data and then use it to produce new results with more datasets. However, when the result is incorrect, there is a need to “teach them” >>>>> more training.
• Deep learning networks do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes. However, even they can be wrong if the data quality is not good enough.
• Data decides everything. It is the quality of the data that ultimately determines the quality of the result.

Also, in the links below, there are some works that used and reviewed both of these techniques

Article From machine learning to deep learning: Progress in machine ...

Article Machine Learning and Deep Learning frameworks and libraries ...

Article Machine Learning and Deep Learning Methods for Intrusion Det...

Conference Paper Classifying Multilingual User Feedback using Traditional Mac...

I hope it will be useful to you

Thank you

Dear Fahad

Thanks for your answer