What is Generative AI, How it is different from existing Machine Learning Techniques
Part 1 : difference between AI and ML , types of ML
Generative AI is a type of artificial intelligence?technology that can produce various types of content,?including text, imagery, audio, and synthetic data.
what is artificial intelligence?and what is the difference between AI and machine?learning?
AI:
One way to think about it is that AI is a discipline,?like physics for example.
?AI is a branch of computer science?that deals with the creation of intelligence agents, which?are systems that can reason, and learn, and act autonomously.
Essentially, AI has to do with the theory and methods?to build machines that think and act like humans.?
ML:
A branch of artificial intelligence called machine learning is used in this industry. A system or program that trains a model using input data.
The same data that was used to train the model as well as fresh or previously unobserved data can be used to generate effective predictions using the trained model. The computer can learn without explicit programming?
What are common classes of machine learning models?
a) unsupervised
b) supervised
c) semi supervised
we see the semi supervised little later, first we discuss about the remaining supervised and unsupervised models?
领英推荐
what are the difference between above two supervised and unsupervised
The primary distinction between the two is the presence of labels in supervised models and no presence of labels in unsupervised models.
Data that has a label, such as a name, a type, or a number, is referred to as labelled data.
For example, let's say you are the owner of a restaurant.?You have historical data of the bill amount?and how much different people tipped based on order type?and whether it was picked up or delivered.?
The above example soled In supervised learning, the model learns from past examples?to predict future values, in this case tips.?So here the model uses the total bill amount?to predict the future tip amount based on whether an order was?picked up or delivered.?
This is an example of the problem?that an unsupervised model might try to solve.?So here you want to look at tenure and income?and then group or cluster employees?to see whether someone is on the fast track.?
Unsupervised problems are all about discovery,?about looking at the raw data and seeing if it naturally?falls into groups.
Let we understand what is semi supervised Learning now
In supervised Learning, Testing data values or x are fed into the model during supervised learning. The output of the model is a prediction, which is then compared against the training data that was used to train the model.
Error is defined as the wide disparity between the projected test data values and the actual training data values.
In order to bring the predicted and actual values closer to each other, the model works to minimize this error. This is a well-known optimization issue.
what is deep Learning?
deep learning?fits as a subset of machine learning methods
deep learning is a type of machine learning?that uses artificial neural networks,?allowing them to process more complex patterns than machine?learning. What are Artificial neural Networks?
Artificial neural networks are inspired by the human brain.?They are made up of many interconnected nodes or neurons?that can learn to perform tasks by processing data and making?predictions. Deep learning models typically have many layers?of neurons, which allows them to learn?more complex patterns than traditional machine learning?models(supervised and unsupervised) .?And neural networks can use both labeled and unlabeled data. This is called semi-supervised learning. In semi-supervised learning, a neural network?is trained on a small amount of labeled data?and a large amount of unlabeled data.