MACHINE LEARNING TUTORIALS

ML Tutorials: AI vs. ML vs. Deep Learning

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Ever since ChatGPT debuted in November 2022, the terms "Artificial Intelligence", "Machine Learning", and "Deep Learning" have been used interchanegably. However, if you ever want to make a difference in the industry, you'll need to understand the real difference between these three terms. In this first tutorial, we'll explore the difference between these commonly used terms.

AI vs. ML vs. Deep Learning




Summary

  • Artificial Intelligence (AI) is a branch of Computer Science that uses computers to simulate human consciousness. AI is an umbrella term for any form of technology that solves problems autonomously by simulating human intelligence.
  • Most of the time, Data Scientists use AI for facilitating Data Analysis-, Manufacturing-, and Customer Service-oriented tasks.
  • AI takes input data to then produce an output for the user (e.g., you give ChatGPT a prompt (input) and it produces a response (output)).
  • Whereas AI encompasses the broad idea of using machines to imitate human intelligence, Machine Learning focuses on the models, processes, and supporting technologies to make those ideas and goals possible.
  • Deep Learning is machine learning at the largest scale, involving millions, sometimes, billions, of training data points used by multi-layered neural networks.
  • Neural Networks are a collection of algorithms designed to process large amounts of data and produce an output with the least amount of error. These are designed to imitate the neurons in the human brain.
  • The primary difference between "Deep Learning" and "Machine Learning" is the number of node layers the input gets passed through before an output is produced. Traditional Machine Learning algorithms use a single layer to produce an output, whereas Deep Learning algorithms use multiple layers to produce an output.

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