Deep learning is a small part of ai

deep learning is a small part of ai

Yes!!! Deep learning is a subset of AI

Hi @BADISA_TEJA , Welcome to Deep Learning.AI Community

Thanks for your post !
So basically you are absolutely correct that deep learning is a small part of Machine Learning and Artificial Intelligence. So deep learning is basically in most general terms the family of algorithms and techniques that use neural networks as their backbone. If you see, depite it being a small part of ML and AI, its among one of the most widely used part of ML because of its high performance and efficiency. Each field where AI could be used to enhance service performance has used Deep Learning in some or the other way, be it in developing recommender systems, in computer vision, Google’s AlphaGo ( Reinforcement Model implemented using deep neural network technology ). etc. Some people might say that it seems to be overrated, that might be because of its everyday and now usage because of which it remains in the limelight. But then one should think that if deep learning is so widely used, then it must be having some great potential…
I hope that you would relate to this fact… Happy Learning !

Amit Shukla



Thanks for asking a good question.

First, let me give you an example of relativity between AI and Deep Learning: consider food as a AI and Deep learning as a specific food like pasta which is also popular!
As you see pasta is a subset of food
Here Deep Learning is a subset of AI.

AI can merely be a programmed rule that tells the machine to behave in a specific way in certain situations. In other words, artificial intelligence can be nothing more than several if-else statements.
An if-else statement is a simple rule programmed by a human. Consider a robot moving on a road. A programmed rule for that robot could be:

if something_is_in_the_way is True:     
else:  ​​​​

So, when we’re talking about artificial intelligence it’s more worthwhile to consider two more specific subfields of AI: machine learning and deep learning .

Deep learning models scale better with a larger amount of data. To paraphrase Andrew Ng, the chief scientist of China’s major search engine Baidu, co-founder of Coursera, and one of the leaders of the Google Brain Project, if a deep learning algorithm is a rocket engine, data is the fuel.

wish you enjoy this information.


Hello! Can you explain a bit more about the difference between ML and Deep Learning? Does ML use if-else statements? If so why is it differentiated from AI in general? Thanks!

There is some truth to that statement, e.g. when it comes to tree-based models: you can think of a random forest as a collection of many if-else statements.

I would suggest to take a look at this thread here:

Machine Learning (ML) is a field or subclass of AI which teaches machines / computers to learn from data w/o being explicitly programmed (like rules or so). That has powerful applications if you think of platforms with network effects where data emerge because these data helps that new knowledge and information can be incorporated into the ML model (by training) which often means that the ML model is getting more powerful over time.

To stay in our logic: the algorithm helps the model to learn the right thresholds and rules for the if else statements in the random forest. Of course there are also more avcanced ML models like gaussian processes that are not just a collection of if-else statements but can also utilize prior information etc. for probabilistic modeling!

Deep Learning models with advanced architectures (like transformers but also architecture w/ convolutional | pooling layers) are designed to perform well on very large datasets and also process highly unstructured data like pictures or videos in a scalable way: basically the more data, the merrier!

Compared to classic ML models DNNs possess less structure and can learn more complex and abstract relationships given sufficient amount of data. However if you have domain knowledge that you can model in a handful of features, classic ML can be very powerful, too. Especially if you have rather a limited amount of data (which typically can be represented in structured tables), see also this thread: Why traditional Machine Learning algorithms fail to produce accurate predictions compared to Deep Learning algorithms? - #2 by Christian_Simonis

Hope that helps, @Anu_Singh!

Best regards

Here also a thread on tree-based models which could be interesting in this context: Can decision tree algo used for regression? - #2 by Christian_Simonis

Best regards