Deep learning vs. machine learning

 Deep learning vs. machine learning

Given how frequently deep learning and machine learning are used interchangeably, it's important to understand their differences. As previously stated, artificial intelligence encompasses both machine learning and deep learning, with deep learning functioning as a subfield of machine learning.

Neural networks are the actual building blocks of deep learning. A neural network with more than three layers—which would include the inputs and the output—is referred to as "deep" in deep learning. Such an algorithm is known as a deep learning algorithm. The diagram below is a broad representation of this.

The method that each algorithm learns is how deep learning and machine learning are different from one another. Larger data sets may be used since deep learning automates a substantial portion of the feature extraction process, reducing the need for manual human interaction. As mentioned by Lex Fridman in the MIT lecture above, deep learning might be conceptualised as "scalable machine learning". Traditional machine learning, often known as "non-deep" learning, relies more on human input to acquire knowledge. In order to distinguish between different data inputs, human specialists establish the hierarchy of characteristics, which often requires more structured data to learn.



Labelled datasets, also referred to as supervised learning, can be used by "deep" machine learning to guide its algorithm, but they are not always necessary. It can automatically discover the hierarchy of characteristics that separate distinct types of data from one another and ingest unstructured material in its raw form, such as text and photos. It can handle data without the need for human interaction, unlike machine learning, which makes it possible to scale machine learning in more intriguing ways.

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