7 Differences Between AI vs ML vs DL: From Interview Perspective

“ Know the basic difference between AI, ML and DL”

Artificial Intelligence (AI) is a multidisciplinary area of research that seeks to advance the development of intelligent machines with the capacity to emulate various cognitive functions exhibited by humans. The field of study encompasses a range of subfields, such as machine learning and deep learning.

Machine learning is a subfield within the realm of artificial intelligence (AI) that specifically concentrates on the advancement of algorithms and models capable of facilitating computers in acquiring knowledge from data and subsequently making predictions or decisions. The process entails the identification and interpretation of patterns and insights from data, without the need for explicit programming.

Deep learning is a distinct branch of machine learning that focuses on the development and utilisation of neural networks, which are designed to mimic the intricate structure and functionality of the human brain. Deep learning algorithms are specifically engineered to autonomously acquire hierarchical representations of data through the utilisation of multiple layers of artificial neurons.

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Although AI, machine learning, and deep learning are closely related, they exhibit notable distinctions. To gain a clearer understanding of these distinctions, it would be beneficial to analyse them in a tabular format.

In essence, artificial intelligence (AI) pertains to the overarching domain concerned with the advancement of intelligent machines. Conversely, machine learning and deep learning constitute distinct subcategories within AI that concentrate on the acquisition of knowledge through data-driven methodologies.

Machine learning encompasses the creation of algorithms that facilitate the acquisition of knowledge by computers through the analysis of data. On the other hand, deep learning employs neural networks to acquire intricate and layered representations of data.

The selection of the appropriate methodology is contingent upon several factors, including the intricacy of the issue at hand, the accessibility of annotated data, and the computational capabilities at one’s disposal.’

In general, these disciplines have fundamentally transformed problem-solving methodologies and possess the capacity to yield substantial progress across diverse sectors.

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