Difference between AI, ML, and Deep Learning

AI or Artificial Intelligence is the current buzzword on the streets. It has applications in almost all domains as well as markets. The private and Government sectors are equally excited about AI. So what is it? 

In simple words, it is a technique, by which a machine or computer can exhibit decision-making intelligence on the basis of data, logic, and reasoning. Mostly, AI is governed by various algorithms for different applications, which are further powered by the computation of huge data. For instance, YOLO (You Only Look Once) is the algorithm by which a machine can detect various objects present in an image within a fraction of a second. Algorithms like this are which have made self-driven cars like Tesla a reality. In AI the algorithms are mostly classified into three categories:

• Classification Algorithms: This type of algorithm is used for administered learning or supervised learning. It divides the subjected variable into different classes to be able to predict the class for a given input. One of the applications is to detect if the email is spam or not
• Regression Algorithms:  This type of algorithm can predict the output values based on input data points fed into the learning system. The common application of this is in the stock market or weather prediction.
• Clustering Algorithms: This type of algorithm is used on unsupervised learning. The prime objective of this algorithm is to classify similar objects into groups. 

 

 

Machine learning or ML is a subset or a branch of Artificial Intelligence. ML is a technique to train huge amounts of data to the system and based on algorithms, get predictive analysis. For instance, we can train the system with various attributes of fruits or flowers. Then we can enter some attributes as input and the system would predict which fruit or flower it is most likely to be. Unlike other computer programs, ML never gives boolean results. The only thing ML can prove is the probability or the likeness of some event to exist.

The Accuracy of a machine learning application is directly dependent on the amount and quality of the training data. In ML the algorithms are mostly classified into three categories based on their pattern of learning:

• Supervised Learning: A model is created by training it with a sample dataset so that it can make predictions. The training process continues until the model achieves the desired level of accuracy on the basis of the training data set.
• Unsupervised Learning:  A model is prepared by deducing structures present in the input data. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
• Semi-Supervised Learning: In this process, there is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

 

 

Deep Learning is a subset of machine learning only, which is dedicated to one specific problem or domain. In other words, if you use ML with a neural network to solve some specific problem, you have deep learning. A neural network is a series of algorithms, which are capable of detecting certain relationships in a set of data. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

 

 

Few applications of Deep Learning can be found in deep fake, cancer detection, Natural Language Processing, and Colorization of black & white or grayscale images.