Read Machine Learning Vs Deep Learning and get to know the difference between machine learning and deep learning with respect to Data Presentation, Complex Queries, Amount of data, Output, work analysis, etc.
Artificial Intelligence is very vast and it has given us so many innovations. Machine learning and deep learning are parts of Artificial Intelligence. Both Machine learning and deep learning are working on algorithms and make use of AI. These are the hottest research topics. These days people are researching more and more on these terminologies. Due to the rapid growth of different technologies, businesses are now looking for technology consulting those companies that strive to give the best for their business.
The expansion of artificial intelligence also produces growth in software development services, IoT applications and blockchain. Currently, software designers are sightseeing new ways of programming that are more prone to deep learning and machine learning.
In this blog, we will discuss the differences between machine learning and deep learning.
| Features | Machine Learning | Deep Learning
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| Definition |
A subset of artificial intelligence involved with the creation of algorithms that can modify itself without human intervention to produce desired output- by feeding itself through structured data.
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A subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers of these algorithms- each providing a different interpretation to the data it feeds on. Such a network of algorithms are called artificial neural networks |
| Data Presentation | Machine Learning makes the use of structured data by using its algorithms | It makes its dependability on layers of ANN (Artificial Neural Networks) |
| Complex Queries | Machine Learning does not solve complex queries as it contains a huge amount of data | Deep Learning can help solve complex queries |
| Amount of data | Machine Learning does not require much data. | Deep Learning requires much data. |
| Number of Algorithms | Machine Learning makes use of many algorithms | Deep Learning makes use of fewer Algorithms |
| Training Time | Machine Learning takes short training time | Deep Learning takes a long training time. |
| Management | The different algorithms are analyzed for examining the data variable in the dataset | Algorithms are self-directed for data analyst |
| Hyperparameter Tuning | It has limited tuning capabilities | It can be tuned in different ways |
| Training Time | It takes less time for training | It takes more time for training |
| Accuracy | It is less accurate | It is highly accurate |
| Hardware Dependency | It trains on CPU | It requires GPU for its training |
| Output | The output is basically in the form of numerical data like classification, score | The output can be anything like a score, free text, sound, and an element |
| Feature Extraction | It is unable to perform automatic feature extraction as it needs labeled parameters | It performs automatic feature extraction without the need for human extraction
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| Work Analysis | It works on a low-end machine | It works on high-end machines |
| Interpretability | Some of its algorithms are easy to interpret | It is difficult to impossible. |
Also Check: Difference Between Data Mining and Machine Learning
Conclusion
Machine learning and deep learning are two subclasses of artificial intelligence. It has gain immense popularity in the last 5 years. Both technologies are trendy to conduct research. In this blog, we have discussed the difference between machine learning and deep learning. If you are having any doubt feel free to ask me in the comment box.
