Deep Learning vs Machine Learning — What’s the Difference?

 



What is deep learning?

Machine learning refers to computers ability to complete tasks without being explicitly programmed... while still thinking and acting like machines. Their capacity to execute some difficult tasks — such as gathering data from an image or video is still far behind that of humans.

Because they've been carefully patterned after the human brain, deep learning models provide an extraordinarily complex approach to machine learning and are geared to tackle these issues. Data is transmitted between nodes (like neurons) in highly coupled ways using complex, multi-layered "deep neural networks ." As a result, the data undergoes a non-linear transformation that becomes increasingly complex.

While it takes a lot of data to ‘feed and create' such a system, it can start producing results almost immediately, and there isn't much need for human interaction once the programmes are in place.

  • Convolutional Neural Networks
  • Recurrent Neural Networks

What is machine learning?

Machine Learning is a subset of artificial intelligence that focuses on a specific goal: instructing computers to accomplish tasks without explicit programming.

Computers (in most situations) are fed structured data and learn to become better at analysing and acting on it over time.

Consider 'structured data' as data inputs that can be organised into columns and rows. In Excel, you could make a category column called "food" with row entries like "fruit" or "meat." This type of 'structured' data is particularly easy for computers to work with and the benefits are evident (one of the most important data programming languages is called structured query language' for a reason).

Once programmed, a computer can take in new data indefinitely, sorting and acting on it without the need for further human intervention.

Even if you cease categorising your data, the computer may eventually recognize that ‘fruit' is a form of food. This self-reliance is so important in machine learning that it divides the field into subcategories dependent on how much ongoing human assistance is required.

  • Unsupervised learning
  • Reinforcement learning

5 key differences between machine learning and deep learning

  • Human Intervention
  • Hardware
  • Approach
  • Applications


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