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AI vs. Machine Learning vs. Deep Learning - 5 Major Differences

AI vs Machine Learning

Machine learning is a subset of Artificial intelligence and deep learning is a subset of machine learning. Both have been revolutionary approaches in bringing dramatic changes in simplifying many of our complex day to day activities in the past few years.

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AI vs machine learning vs deep learningAI vs Machine learning vs Deep learning

Table of Contents

Machine learning

Machine learning refers to the ability of computers to learn without the need to be explicitly programmed. Machine learning algorithms are dynamic, can modify themselves and require minimal human intervention.

The fuel source for any machine learning model is the input data. This data might have labels to it (for example, these are images of a cat, these are not images of a cat). The ML algorithm tries to see patterns in the data and build a model. This model (also known as a classifier) will try to predict and classify a given input image. This method of learning from a given set of labeled data is known as supervised learning. Methods which do not require structure or labeled data are known as unsupervised methods.

Deep Learning

Deep learning is a subset of machine learning that tries to mimic the human brain while solving problems. They are more accurate in predicting outcomes as compared to traditional machine learning models.

The term "deep" refers to the use of several layers of neural networks in deep learning algorithms. The number of layers contribute to the depth of the neural network. Multiple layers in a neural network help in learning features of the data. Deep learning processes are highly computation intensive and require high performance GPUs.

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5 Key differences between AI, Machine learning and deep learning

Data Size

Traditional ML algorithms on general perform great on small to medium sized data sets whereas deep learning algorithms would need huge data sets for better performance.

Machine requirements

Traditional ML algorithms can be run on a low end machine whereas deep learning methods require high performance machines with GPUs.

Feature engineering

Features are individual attributes of the data and feature engineering is the process of using the domain knowledge of the data to create more features to facilitate machine learning. Traditional ML methods require manual feature engineering to be done whereas deep learning methods can perform feature engineering automatically.

Model training time

Due to the large data set size and multiple layers of neural networks, deep learning methods take more time to train data. Deep learning methods could take up-to weeks or months depending on the task at hand.

Interpretability

Using traditional ML methods, it is easy to say why a certain prediction was made. This is nearly impossible to explain in deep learning methods. This can be a huge barrier in heavily regulated verticals such as banks where interpretability is very much needed.

Machine learning vs deep learning-1Machine learning vs deep learning

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