Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL), can be imagined as the three bears from Goldilocks staying together in a house, where each member has a specific use but yet conceptually they are interconnected. Artificial Intelligence is the big umbrella under which resides the Machine Learning concepts, and Deep Learning can be referred to as a sub-set of Machine Learning. So while Deep Learning and subsequently Machine learning comprises of Artificial Intelligence, the other way around is not necessarily true.
The field of data science is buzzing with these terminologies, and in all the noise it is very easily misinterpreted, and often Machine Learning and Deep Learning are used interchangeably for one another. One thing is certain that Deep Learning is a technique for implementing Machine Learning.
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Let us understand this by breaking down the puzzle….
Artificial Intelligence orchestrates the capacity of a machine to comprehend complex human tasks like, decision making, understanding spoken language, detecting fraud, in short, it enables a machine to imitate intelligent human behaviour.
Machine Learning is surely a part of AI, where it enables a computer to act in a specific way without the need for explicit programming. This is imperative as the volume of data is ever increasing and to keep up, the machine should have the capability of implementing effective algorithms which can effectively and efficiently make predictions by recognising patterns. To perform the same, data scientists have a number of existing ML methods or Algorithms which can be easily applied to any data problem, at the same time it can be applied to a number of real-life use cases, for example, recommendation engines, or applying Natural Language Processing (NLP) in chat logs.
Deep Learning is a subset of ML and when data scientist refers to the term deep learning they most often mean Deep Artificial Neural Networks or alternatively Deep Reinforcement Learning.
Deep Artificial Neural Networks are essentially a set of Algorithms which are popular in recent times, for setting new records in accuracy while dealing with complex problems like, Image Recognition, Sound Recognition, Recommendations Systems, etc…, To add on, one can easily define DL in a similar manner to ML, so it can be safely said that DL also enables a computer to act in a specific manner without the need of explicit programming, with the addition that DL ensures it produces results with higher accuracy.
DL is often more complex as compared to ML, the prerequisites to DL would be a high-performance computer and huge volumes of Labelled data to give reliable results.
ML can be used with a small volume data set and has a shorter training time, while DL will be effective with large volume data set and often requires a longer training time. In ML you can use your own features, so for example, if you need a computer to be trained on recognising an image of a cat, you will first need to key in all the relevant features of a cat into it. In DL you will need to feed the computer with large volumes of data with cat images, and the system will choose the features on itself which characterises a cat. Hence the training time is longer, while the chances of accurate information are higher due to the complexity of reaching the conclusion, in some cases, it is more accurate than humans.
Driverless Cars, Movie Recommendations and preventive health care are all possibilities enabled by Deep Learning. DL in the future could be responsible for machine assistance in possibly all aspects of life. Deep Learning has the promise of taking the application of AI from fantasy to reality.
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