With the recent advancements in technology, the concept of Artificial Intelligence has been upgraded with the introduction of different algorithms and learning mechanisms. Two such learning mechanisms are Machine Learning and Deep Learning. Machine Learning and Deep Learning are practices that are more similar than not.
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What is Machine Learning?
Machine Learning belongs to the aggregated set of techniques related to Artificial Intelligence (AI). It uses algorithms that build models by parsing data and eventually use the obtained parameters to make predictions. Strategies involved include clustering, Bayesian approaches, decision trees and regression.
Though Machine Learning started off as a fairly small and insignificant part of Artificial Intelligence, its importance in the field of research has grown leaps and bounds in the last couple of years. Machine Learning, coupled with hardware support has made it widely adopted as a technological advancement.
Today, Machine Learning is used for a variety of applications.
Some of them include:
- Pattern Recognition and Computer Vision
- Machine Learning Algorithms to determine patterns by Social Media websites such as Facebook.
- Easy and optimal filtering out of data through search engines such as Google.
What is Deep Learning?
One of the approaches to Machine Learning was the introduction of artificial neural networks to determine algorithms. However, this was not given as much importance in the past until its capabilities were brought to the forefront. Neural Networks in the brain helps stimulate the activity of the neurons present in a very systematic and layered fashion. Thus, Deep Learning uses a similar approach for data propagation and thus enables machines to make predictions that are a lot more accurate and detailed. A major advantage of using Deep Learning is that larger volumes of data can be handled with greater precision. Also, the probability of negative or false models is completely eliminated.
Major applications of Deep Learning include:
- Large-scale business applications.
- Autonomous recommendation systems.
- Image recognition applications that rely on Computer Vision principles.
Comparison between Machine Learning and Deep Learning:
Both Machine Learning and Deep Learning are similar in the sense that they are both subsets of Artificial Intelligence. Thus, the basic principle of both these learning practices is the collection of information and data for making informed decisions as and when required. However, some of the stark differences between the two are:
- On the basis of applications: Machine Learning works best for systems and applications where the priority is to learn from the data collected and use that learning for a particular task. Thus, Big Data analysis and data mining are best suited for Machine Learning.
- On the other hand, those applications that are said to belong to more of a niche-category which simply means that the data is a large corpus of image or texts uses Deep Learning algorithms. This includes using Graphics Processing Units to create models for specialized video and image recognition tasks such as in the case of navigation and autonomous driving.
- On the basis of Hardware: Hardware and high-end machines are needed to incorporate Deep Learning as opposed to Machine Learning. This is because Graphics Processing Units do complex matrix multiplications and other algorithm interpretations which is not the case with simple Machine Learning.
- On the basis of execution time: Deep algorithms, due to their complexity and number of parameters that are processed takes a longer time to train. On the other hand, Machine Learning takes a lot lesser time, ranging from a few seconds to a couple of hours.