What are the differences between Artificial Intelligence, Machine Learning and Deep Learning?

Artificial Intelligence is purely math and scientific exercise, but when it became computational, it started to solve human problems formalized into a subset of computer science.

Machine Learning is that instead of engineers “teaching” or programming computers to have what they need to carry out tasks, that perhaps computers could teach themselves -learn something without being explicitly programmed to do so. ML is a form of AI where based on more data, and they can change action and response, which will make more efficient, adaptable and scalable.

Deep Learning is a technique for implementing ML. ML provides the desire output from a given input, but DL reads the input and applies it to another data. In ML, we can easily classify the flower based upon the features. Suppose you want a machine to look at an image and determine what it represents to the human eye, whether a face, flower, landscape, truck, building, etc.

Machine learning is not sufficient for this task because machine learning can only produce an output from a data set –whether according to a known algorithm or based on the inherent structure of the data. You might be able to use machine learning to determine whether an image was of an “X” — a car, say — and it would learn and get more accurate. But that output is binary (yes/no) and is dependent on the algorithm, not the data. In the image recognition case, the outcome is not binary and not dependent on the algorithm.

Seven Differences Between Machine Learning and Deep Learning

Machine learning and deep learning are two important areas of artificial intelligence. Let’s now look at the differences between these two fields.

1- Data amount

The most important difference between machine learning and deep learning is the amount of data. While machine learning algorithms work with small or medium-sized data, larger data is required for deep learning algorithms.

2- Hardware

While low or medium computers are sufficient to do machine learning analysis, powerful computers are needed to perform deep learning analysis.

3- Attribute engineering

While feature engineering is required for machine learning, feature engineering is not needed for deep learning. In other words, some features may be related to each other while performing machine learning analysis. This relationship can harm your analysis. So it’s important to set features for machine learning analytics, but not for deep learning.

4- Training time

Since machine learning algorithms work with small or medium-sized data, the training time is short (one or two hours at most). But because deep learning algorithms use big data, the training time is long (days or weeks).

5- Scaling

While simpler problems can be solved with machine learning, complex problems such as face recognition and translation from language to language can be solved with deep learning.

6- Data structure

While machine learning techniques can only analyze structured data, deep learning algorithms can also analyze unstructured data such as images, audio or video.

7- Algorithm structure

Some machine learning algorithms, such as regression, are known how to work. But deep learning algorithms are a black box and how they work is inexplicable.

References

Geron A., 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow.

github.com/iNeuronai

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