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Deep Learning vs Machine Learning, Neural Network

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JUNGSHIC PARK edited this page Dec 7, 2022 · 3 revisions

Reference: IBM(https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks)

Key Understandings

  • Deep Learning needs less intervention by humans than Machine learning.
  • Deep Learning uses Neural Networks
  • Deep Learning is a subset of Machine Learning

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Relation diagram

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  • machine learning is a subfield of artificial intelligence.
  • Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms.
  • In fact, the number of node layers, or depth, of neural networks distinguishes a single neural network from a deep learning algorithm, which must have more than three.

Neural Network?

  • mimic the human brain through a set of algorithms.
  • four main components: 1)inputs, 2)weights 3)a bias or threshold, 4) output.

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Reference: Microsoft(https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning)

Deep learning

Deep learning is a subset of machine learning that's based on artificial neural networks. The learning process is deep because the structure of artificial neural networks consists of multiple input, output, and hidden layers. Each layer contains units that transform the input data into information that the next layer can use for a certain predictive task. Thanks to this structure, a machine can learn through its own data processing.

Machine learning

a subset of artificial intelligence that uses techniques (such as deep learning) that enable machines to use experience to improve at tasks. The learning process is based on the following steps:

  1. Feed data into an algorithm. (In this step you can provide additional information to the model, for example, by performing feature extraction.)
  2. Use this data to train a model.
  3. Test and deploy the model.
  4. Consume the deployed model to do an automated predictive task. (In other words, call and use the deployed model to receive the predictions returned by the model.)

Artificial intelligence

  • (AI) is a technique that enables computers to mimic human intelligence. It includes machine learning.

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Reference: https://www.geeksforgeeks.org/difference-between-machine-learning-and-deep-learning/

Machine Learning

  • improves from experience without being programmed to that level. Machine Learning uses data to train and find accurate results.
  • focuses on the development of a computer program that accesses the data and uses it to learn from itself

Deep Learning

  • The algorithms are created exactly just like machine learning, but it consists of many more levels of algorithms.
  • All these networks of the algorithm are together called the artificial neural network.

Examples

ML: Banks, doctor’s offices, and mailboxes all employ machine learning already. DL: autonomous algorithms, such as self-driving automobiles or surgical robots.

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What is a neural network? Reference: https://aws.amazon.com/what-is/neural-network/#:~:text=A%20neural%20network%20is%20a,that%20resembles%20the%20human%20brain.

What is a neural network?

  • A neural network is a method
  • Teaches computers to process data in a way inspired by the human brain.
  • A machine learning process called deep learning that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
  • Used to learn from their mistakes and improve continuously.
    • Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.

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