It’s hard to ignore the cultural and organizational impact that Artificial Intelligence (AI) has had over us. Most organizations today have realized the impact of AI, and are doing all that they can to participate in and help facilitate the growth of the technology.
For those who know the nuances of AI and the metrics involved in it, Deep Learning and Machine Learning may not look like challenging terms. But, for those who are new to AI, these terms might be hard to understand. To understand the complications organizations face when adopting machine learning, we must first fully understand the difference between deep learning and machine learning.
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What are Machine Learning and Deep Learning?
Machine Learning is the science that deals with getting computers to perform in a specified manner, without meddling with their programming capabilities. The uniqueness of machine learning lies in this very definition. Data usually changes on a routine basis. And, with the rapid pace at which it is accumulating, computers programmed to do specific tasks cannot adjust to the pace. This is where machine learning comes into the picture, as it implements algorithms that help recognize patterns on a real-time basis. Once these patterns are recognized, the system can make healthy predictions from them.
We can apply numerous machine learning algorithms for all kinds of data problems. Techniques, including logistic regression, linear regression, random forests, k-mean clustering, and decision trees, can be applied to real-life use cases to gather actionable insights. Read Applications of Data Science, Deep Learning, and Artificial Intelligence for more thorough examples.
Deep learning can be thought of as a part of machine learning that has a lot to do with your brain. Since it mirrors the dimensions of our brains, the method is particularly effective in detecting features. This means feeding the model a large volume of data but without defining all the features as you would have to do with a linear regression model for machine learning.
This can translate into real-life examples as well, where your learning model works without gathering a huge number of features. For example, imagine that you want to classify images of dogs. For this, you’ll have to feed the model with hundreds of images of dogs but wouldn’t have to define features for it. You won’t have to tell the machine what features make a dog a dog.
Deep learning models are not meant to be trained with an algorithm. Instead, they make learning a step further. Deep learning models work directly with audio, images, and video data to get real-time analysis. The data being fed to the deep learning model doesn’t need any external intervention or initial preparation. You can feed raw data to the model and receive actionable insights.
How Deep Learning Can Fill the Machine Learning Gaps
While machine learning continues to solve many data problems today, it’s still a new technology with many limitations. Deep learning can aid where machine learning falls short.
Based on the shortcomings of Machine Learning, mentioned above, Deep Learning is perfect for filling the gap. By bringing feature engineering and unsupervised learning to Machine Learning, Deep Learning ensures that these shortcomings are met competently.
Data scientists can benefit from including Deep Learning as a subtype of Machine Learning and ensuring that they achieve the best of both worlds by using both of these data analysis methods together.
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