Difference 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.
