Found 377 Articles for Artificial Intelligence

Do you know how much data a person is creating every second? The numbers are astonishing. According to Domo, during 2020 every person created 1.7MB of data every second and it is not going to slow down in the future as well. It would not be wrong to say that we are living in the 'age of data'. One of the biggest challenges in front of businesses and organizations is to make sense of all the data. They are trying to deal with it by creating intelligent systems using the concepts and methodologies from Machine Learning (ML), one of the ... Read More

The objective of search in Artificial Intelligence (AI) is to find the path to solve different problems. The search in AI can be executed in two ways namely, Forward Reasoning and Backward Reasoning. The most basic difference between the two is that forward reasoning starts with the new data to find conclusions, whereas backward reasoning starts with a conclusion to determining the initial data. Read this article to learn more about Forward Reasoning and Backward Reasoning and how they are different from each other. What is Forward Reasoning? Forward reasoning is a process in artificial intelligence that finds all the ... Read More

In this post, we will understand the difference between linear regression and logistic regression.Linear RegressionIt helps predict the variable that is continuous, and is a dependent variable.This is done using a given set of independent variables.It extrapolates a line to find the value of dependent variable.Least square methods are used to estimate the accuracy.The best fit line is found, that helps predict the output.It is generally a continuous value.The relation between the dependent variable and independent variable has to be linear.The independent variables may have collinearity between them.It is considered a machine learning problem, i.e an applied statistics problem.Logistic RegressionIt ... Read More

The idea behind using gradient descent is to minimize the loss when in various machine learning algorithms. Mathematically speaking, the local minimum of a function is obtained.To implement this, a set of parameters are defined, and they need to be minimized. Once the parameters are assigned coefficients, the error or loss is calculated. Next, the weights are updated to ensure that the error is minimized. Instead of parameters, weak learners can be users, such as decision trees.Once the loss is calculated, gradient descent is performed, and tree is added to the algorithm step wise, so that loss is minimal.Some examples ... Read More

Face recognition is the task of identifying and verifying people present in a photograph based on their face. This is a trivial task for humans, even if the lights are varying or when faces change due to age or they are obstructed with accessories, facial hair and so on.But it remained a fairly challenging computer vision problem until a few years back. Deep learning methods have been able to leverage large datasets of faces and learn various representations of faces, thereby allowing modern learning models to perform well and better.Facial recognition may be used to identify person in a photograph ... Read More

A neural network can contains any number of neurons. These neurons are organized in the form of interconnected layers. The input layer can be used to represent the dataset and the initial conditions on the data.For example, suppose the input is a grayscale image, the output of every neuron in the input layer would be the intensity of every pixel of the image.This is the reason we don’t count the input layer as a part of the other layers in the neural network. When we refer to a 1-layer net, we actually refer to a simple network that contains one ... Read More

A neuron is a mathematical function that takes one or more values as input and outputs a ingle numerical value −It can be defined as follows −Here, ‘f’ refers to the function.We first computed the weighted sum of the inputs xi and the weights wiThe weight wi is also known as the activation value or activation function.The input xi can be a numerical value that represents the input data or it can be an output from other neurons if the neuron belong to a neural network.The weight wi is a numerical value that can be used to represent the strength ... Read More

A neural network can be understood as a network of hidden layers, an input layer and an output layer that tries to mimic the working of a human brain.The hidden layers can be visualized as an abstract representation of the input data itself. These layers help the neural network understand various features of the data with the help of its own internal logic.These neural networks are non-interpretable models. Non-interpretable models are those which can’t be interpreted or understood even if we observe the hidden layers. This is because the neural networks have an internal logic working on its own, that ... Read More

The basic example of a neural network is a ‘perceptron’. It was invented by Frank Rosenblatt in 1957. The perceptron is a classification algorithm similar to logistic regression. This because, similar to logistic regression, a perceptron has weights, w, and an output function, ‘f’, which is a dot product of the weights and the input.The only difference is that ‘f’ is a simple step function, where a logistic regression rule is applied to the output of the logistic function. On the other hand, perceptron can be understood as an example of a simple one-layer neural feedforward network.The perceptron was considered ... Read More

Neural networks have been around for many years, through which they have been praised as well as criticised for their characteristics.But off late, they have gained attention over other machine learning algorithms. Of course, Machine learning algorithms are important as they help achieve certain goals. But what should we do when machine learning algorithms can’t achieve higher accuracy?This is where deep learning algorithms come into play. They mimic the layers of the human brain, and try to take optimal decisions by passing an input from one layer to the next.Neural networks, as the name suggests, tries to follow the pattern ... Read More