Deep Learning vs Machine Learning
Deep learning vs machine learning is an interesting comparison but quite easy to understand even if both take advantage of big data equally today. Traditional machine learning applied feature engineering before modeling but this required expert knowledge, is time-consuming, and a often long manual process. In addition it requires often 90% of the time in application areas and is sometimes even problem-specific and domain-specific. Deep Learning in contrast enables feature learning promising a massive time advancement. More general information about deep learning can be obtained from our article deep learning. The following picture illustrates the summary of the approach using data, features, and the model itself as explanation.
In order to provide an example consider huge set of images with cars. The algorithms learn the inherent structure of cars by performing feature learning. Feature learning in this case can refer to learn the feature of round tyres that typically cars have. Hence, compared to other machine learning algorithms were features are engineered beforehand, the deep learning algorithm learn various features automatically. This in turn leads to another difference related to the quantity of datasets. Deep learning algorithms take advantage of large quantities of datasets and can truly benefit from big data. In traditional machine learning data was rather often reduced or transformed into features that are much lower in size, dimension, or sample quantity. The reasoning behind this was also often the problem of having to little computational time or that more data is not always better data due to noise and other factors.
Deep Learning vs Machine Learning Details
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