Human Involvement or Intervention
In the case of a machine learning system, human involvement is required, where an individual identifies and hand-codes the laid characteristics based on the data type. Whereas a deep learning system automatically tries to understand and learn those features, not requiring any human intervention or involvement. Let's take help from a facial recognition software example to understand this better. Here, the program first understands and learns how to detect and recognize face lines and edges and then hops onto other important parts of the face. Finally, it goes to the overall face representation. We must understand that the data usage in this is enormous. With the passage of time, as the program trains itself, the possibility of getting more accurate and correct recognition increases by notches.
Leveraging Hardware
Deep learning leverages a more robust hardware when compared to simpler ML systems. The reason for the same lies in the fact that a mammoth amount of data is getting processed and the mathematical calculation complexities concerned in the algorithms leveraged. A prominent example of hardware leveraged by deep learning is a GPU (graphical processing unit). On the other hand, machine learning systems run on low-end machines without requiring as much computing power.
Time Consumption or Requirement
By now, you have understood that in the case of deep learning, there are massive data sets as requirements, and there are various parameters and intricate mathematical formulas involved. Therefore, a DL system will require a lot of time for training purposes (a few hours to a few weeks). On the other hand, time consumption by machine learning systems ranges from a few seconds to a few hours.
Method or Approach Followed
In the case of deep learning, the systems view an entire problem in one fell swoop. On the contrary, in the case of machine learning, the systems parse data into parts, which are then combined to receive a solution or the result. Let's understand this with an example. Let's suppose you want a program to identify a certain object in an image, say a license plate on a car parked in the parking lot. Taking ML into consideration, you would require to undergo two steps: object detection and recognition. However, with DL, you just need to input the image, and with effective training, the program returns the identified object and its location in the image in one result.
Various Applications
It's believed that if you've gone through the differences mentioned above, you now understand that both these concepts have different applications.
Machine Learning
- Predictive programs (forecast stock market price)
- Email spam identifiers
- Evidence-based treatment plans for better medication solutions for patients
Deep Learning
Apart from the examples discussed in the earlier points (Netflix, music-streaming service, facial recognition), self-driving cars are another great example of deep learning systems. Here, the system leverages various neural network layers to perform actions such as determining objects to avoid, identifying traffic lights, and, most importantly, understanding when to speed up or slow down.
Final Words
Machine Learning and Deep Learning are the latest hot topics, and they are here to revolutionize customer experience, business automation, and how we live our lives. If you are an aspirant wishing to get into any of these domains, there's no time better than today. Being recognized as the best data science institute in Bangalore, Skillslash provides the best data science course with placement guarantee. With live classes led by industry leaders, be sure to learn these concepts in detail, and secure a high-paying job. Contact Skillslash’s support team to know more.
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