Machine learning vs Deep Learning

Machine learning (ML) and deep learning (DL) are two subsets of artificial intelligence that have received much interest in recent years. This article aims to explain, in simple terms, what differentiates deep learning from machine learning, and how you can use these technologies for business opportunities.

Deep Learning vs Machine learning

Before diving deep into the difference between these two terms, let’s acquaint yourself with a basic understanding of what the terms ML and DL mean.

ML is a sub-category of artificial intelligence engaged in developing algorithms that may update themselves without human involvement. DL is a machine learning sub-category where algorithms are generated and work in the same manner as machine learning. There are, however, several layers of these DL algorithms. Each layer provides a unique understanding of the input data. Such networks of algorithms are called artificial neural networks (ANNs). The term neural network is used since the ANNs attempt to imitate the neural networks' function in the biological brain.

Applications of Machine and Deep Learning

ML uses properly structured labeled data. Distinctive features of the data are labeled. First, the ML model is trained using various optimization algorithms, and then it continues to classify features in other data. ML is preferred when the available training data can be structured easily; you want to leverage your business’s artificial intelligence benefits.

Deep learning networks, on the other hand, do not require structured/labeled data for classification. The artificial neural networks transmit the information across various network layers using deep learning, with each network describing the essential characteristics of the data. DL is preferred when it is impossible to label an immense amount of data, the application is too complicated, or the necessary computational resources are available.

Main Difference between Machine and Deep Learning

The essential differentiation between ML and DL arises from how the machine is provided with information. Algorithms in machine learning mostly require standardized data, whereas deep learning depends on multiple ANN layers.

Machine learning algorithms are programmed to be trained on labeled data to do stuff and then utilizing it to generate multiple results with additional data sets. However, where the output quality is not acceptable, they need to be retrained by human interaction. Since machine learning algorithms need labeled data, they are not sufficient for solving complicated queries involving large sets of data.

The interconnected layers in the neural networks sort data into hierarchies of various principles, enabling the networks to learn through their mistakes. Deep learning networks don't involve human interference. However, if data consistency is not high enough, deep learning models are vulnerable to faulty outcomes. It is data accuracy that essentially defines the effectiveness of the process.

While deep learning networks may solve a small problem, they are intended for highly specialized complex applications. They are only appropriate for complicated calculations rather than simple ones, given the number of levels, hierarchies, and operations that these networks can process. Deep learning requires a lot more information about an application than the conventional algorithm in machine learning.

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