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1 June, 2020
Difference between Artificial Intelligence, Machine Learning and Deep Learning

Diffrerence between Artificial Intelligence , Machine Learning and Deep Learning

Difference between Artificial Intelligence, Machine Learning and Deep Learning

We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like “Machine Learning” and “Deep Learning,” sometimes used interchangeably with artificial intelligence. As a result, the difference between artificial intelligence, machine learning, and deep learning can be very unclear.
I’ll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they’re different. Then, I’ll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion.

So what is the Difference between Artificial Intelligence , Machine Learning and Deep Learning

Machine Learning? What it is?

Machine learning is a sub field of artificial intelligence (AI). The aim of machine learning generally is to understand the data structure ( structure of data) and fit that data into models that can be understood and utilized by people.

  • There are lot of questions are coming in our conscious mind about machine learning.
  • Are you aware about it ? Obviously Yes, according to me! , thats why you are reading this.

Anyway, keep reading

Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given.

For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The classification algorithm used to detect handwritten alphabets could also be used to classify emails into spam and not-spam.
“A computer program is said to learn from experience E with some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” -Tom M. Mitchell.

  • Consider playing checkers.
                  E = the experience of playing many games of checkers
                  T = the task of playing checkers.
                  P = the probability that the program will win the next game.
  • Examples of Machine Learning
                    There are many examples of machine learning. Here are a few examples of classification problems where the goal is to categorize objects into a fixed set of categories.
  • Face detection: Identify faces in images (or indicate if a face is present).
  • Email filtering: filter emails to check spam emails.
  • Medical diagnosis: Diagnose a patient to check whether He have disease or not.
  • Weather prediction: Prediction of weather, whether it will rain today.

Some Other Applications of Machine Learning

  • Predicting security breaches, finding malware and other anomalies in data.
  • Personalized recommendations (ex: Netflix, Amazon).
  • Improving online search results based on preferences.
  • Natural language processing.
  • Smart cars and smart homes (IoT).

Read Full article on Machine Learning


Deep Learning

deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics.
While traditional machine learning algorithms are linear, DL algorithms are stacked in a hierarchy of increasing complexity and abstraction. To understand DL, imagine a toddler whose first word is dog. The toddler learns what a dog is (and is not) by pointing to objects and saying the word dog. The parent says, “Yes, that is a dog,” or, “No, that is not a dog.” As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler does, without knowing it, is clarify a complex abstraction (the concept of dog) by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.

Difference between Artificial Intelligence , Machine Learning and Deep Learning

Examples of deep learning applications

Because DL models process information in ways similar to the human brain, models can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, NLP processing and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.
Deep Leaning is also an aspect of Artificial Intelligence.Deep Learning is a sub field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

If you are just starting out in the field of DL or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990’s and early 2000s. The leaders and experts in the field have ideas of what deep learning is and these specific and nuanced perspectives shed a lot of light on what deep learning is all about.In this post, you will discover exactly what deep learning is by hearing from a range of experts and leaders in the field.

Read Full article on Deep Learning

What is Artificial Intelligence?

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AI Definition ( artificial intelligence definition ) –

The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that AL in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.


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