Artificial intelligence is a perfect mixture of fine concepts and technology. The topic of the article is about subsets of Artificial Intelligence that is machine learning and deep learning.
Fourth Industrial Revolution, a blessing of Artificial Intelligence has exceptionally altered our lifestyles. What Artificial Intelligence is actually? Simply put, artificial intelligence is any and every technology that mimics certain human behavior and characteristics. Is it really possible for a machine act and think like a human?
It actually is possible, artificial learning has made it possible. Many artificial intelligence systems are powered by machine learning and some by deep learning. In the coming decades, the impact of Artificial intelligence will be far reaching, it will encompass all the industry like entertainment, advertising, healthcare, transportation, finances, retailing, manufacturing, gaming industry, etc.
It is going to transform its traditional way of processing and would shift its core processing by basing it on machine learning. It is important for the company to keep up with the trend and current demand of adopting measures and techniques that ensure your survival in the times to come.
Don’t take Artificial Intelligence lightly, anyone who isn’t in touch with Artificial Intelligence system enveloping both machine learning technology and deep learning technology is doomed to fall out of the league of becoming top business.
There are several examples of Artificial Intelligence which are currently in use to make our lives much easier and better. To name a few, some are Siri and Cortana, a smart personal assistant; automated responder in customer service, plagiarism checker, voice to text feature, security surveillance system, etc.
As more companies lean into leveraging these technologies, there will be an increased need to hire those with advanced degrees in artificial intelligence to keep up with demand. This will further the growth and uses of the technology in a perpetual manner.
In this article we are going to talk about one of the many ways the artificial intelligence can be achieved that is Machine learning. And we are also going to talk about what is the difference between machine learning and deep learning. Let us first talk about the broader picture and what are the different ways artificial intelligence can be achieved.
First comes machine learning, machine learning has three subsets: a) deep learning b) supervised c) unsupervised. Another way is natural language processing (NLP), it has five subsets in it: a) content extraction b) machine translation c) question answering d) classification e) text generation. Next up is an expert system, planning, robotics. Last but not the least is vision, vision includes image recognition and machine vision, and speech which comprised of speech to text and text to speech. The topic of machine learning is so much more encompassing and you can learn more about it by learning more about machine learning on platforms like Udemy
What is difference between Machine Learning and Deep Learning?
Before understanding what issue difference between deep learning and machine learning, first know about machine learning.
Machine learning png is one the most important method. To easily understand machine learning method, it is just like a child’s way of responding to the outside world. A kid gradually understand things by repeated learning and training.
Let’s take for example, you wanted the kid to recognize what is cat. So you should the image of cat multiple times and make him learn that it is a cat. In the same way, it works for machine learning method. Here the objective is defined and the objective is achieved through the step by step learning by machine by training it.
Machine Learning Vs Deep Learning
For any company who is to adopt for machine learning and deep learning, it is important that they know and understand what is the difference between deep learning and machine learning. Machine learning png is the concept where the goal or objective is told or defined, then the algorithm breaks down the information into small components, it is further learned through step by step learning through proper training and after learning, it is then applied for making informed decisions.
I’ll give you an example by which you can easily understand what is machine learning. Do you watch movie or series or daily soap on a particular app such as say on Hotstar. You watched some movies on it, it put into action an algorithm that which slowly learns about your preferences and on the basis of your watched movies, it will display some movie option with the heading saying you may like to watch. The system first learned about your choices and after making an informed decision on the basis of your provided data put for some desired options.
But in case of deep learning, it is not the matter. One should know that deep learning is a subset of machine learning. In deep learning, the algorithm doesn’t need to be guided. It doesn’t need to be provided with the information and isn’t taught to how to calculate and make predictions. The algorithm in deep learning uses its own data processing to learn. It is this technology credited for the invention of driverless cars. It works in the same manner as what naturally comes to humans.
Driverless cars are able to recognized traffic signal, difference between lights as what symbolizes what, ability to make difference between the passerby and a still object. Deep learning is highly credited for being delivering accurate performance.
Deep learning uses millions of data points for the analysis which is different in machine learning. Deep learning has enabled the fast incorporation of enough data in the computers in order to train huge neural networks. The scalability of deep learning has done wonders.
It became viable to build large neural networking and training is provided to them, there is significant improvement in the performance of the system as we keep on feeding the system with more and more data. It is different from machine learning as the scale of operability is far more higher than what machine learning can do.
This is one of the striking differences between deep learning and machine learning. In machine learning, there is a bar to which the performance is utilized to its utmost potential while in deep learning, the performance continues to enhance and improve as more and more information is provided.
Examples of deep learning algorithms that are currently in practice include language recognition, changing black and white images into colorful pictures, automated translation of the text, deep learning robots, etc.
Following are the striking difference between machine learning and deep learning.