Comparing Deep Learning and machine learning
Published on November 7, 2022
Defining Deep Learning and comparing with machine learning provides us the difference between their core concepts and analytics statistical power to comprehend their relationship. In today’s growing complexity of technologies interfaces between and from data gets generated and analyzed demands sophisticated requirements to deal with it. As we have learned that Deep learning is an extension of neural network which contains input, hidden and output layers, whereas deep neural network composed of multiple different hidden layers since they must handle the complexity.
Deep Learning is all about the brain and how it works. Human brains intercept external signal through different sensory inputs with the help of neurons which helps us to decide what action should be performed. In a similar way we have concept of neurons, concept of layers, and concept of interconnectivity.
Deep Learning:
Deep Learning and Artificial Intelligence
Neural network and deep learning are composed up of these three important elements. From perspective of machine learning, deep learning is considered as a branch of neural network which learns from unstructured and unlabeled data. And today, deep learning is being used across industries, across domains to facilitate various type of innovative implementations.
Deep Learning and related to Artificial Intelligence
Artificial Intelligent is a technique that enables computer to mimic human behavior, on the other hand machine learning is the part of an AI which provides computers the ability to learn without being explicitly programmed.
Deep learning relates to the machine learning to solve complex problems as compared to machine learning and machine learning relates to artificial intelligence. Therefore, Deep learning possess all the features and expectations of artificial intelligence. Artificial intelligence has been evolving since 1950s and the concepts of machine learning and deep learning are maturing fast in this world.
Deep Learning vs Machine Learning
A simple understanding of DL and ML and how they co-related each other, Deep learning contains of minimum three layers whereas machine learning works with one input, one output with one hidden layers whenever non-linearity is required. On the other hand, Deep learning handles complexity and works with large volume of unlabeled data, which requires high-performance hardware. On the contrary, machine learning doesn’t require high-performance hardware to perfume the task.
Deep learning provides the ability to create new features, but machine learning doesn’t have the capability to create new features without human intervention to identify the features. Deep learning, on one hand, provides a complete end-to-end problem solution, machine learning believes more in modularity. It divides tasks into small portions to handle that complexity. In perspective of training, deep learning is more time consuming due to the volume of data and complexity that it takes cares, whereas machine learning, compared to deep learning is quite fast.
Deep Learning:
Machine Learning:
Criteria comparison of Deep learning and Machine learning
We learned deep learning vs machine learning simple way, now will try to understand criteria-base comparison between them. As far as working mechanism in machine learning goes, it utilizes algorithms to train and predict future decisions and functions are modeled using input data. Whereas deep learning uses automated interpretation of data features which is done by identifying relations using neutral network.
In terms of, management machine learning needs manual intervention to examine different variables in the dataset. Whereas deep learning has the capability to self-direct algorithms after implementation to analyze the data. Similarly, machine learning involves a few thousand data points for analysis, whereas deep learning involves million data points for analysis. From output perspective, in machine learning you'll find that output is generally numeric. But in deep learning output can be score, free text, sound, video, images, and various other types.
| Criteria | Deep Learning | Machine Learning |
|---|---|---|
| Working mechanism | Utilizes automated algorithms that are trained to predict future decisions Functions are modelled using input data | Involves interpreting data features and their relationships using neural networks |
| Management | Manually direct algorithms to examine different variables in the datasets | Capable of self-directing algorithms after implementation to analyze data |
| Data points | Involves few thousand of data points for analysis | Involves few million data points for analysis |
| Output | Output is generally numerical | Output can be a score, element, free text, sound, image etc. |
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