Found 664 Articles for Machine Learning

Bokeh is a Python package that helps in data visualization. It is an open source project. Bokeh renders its plot using HTML and JavaScript. This indicates that it is useful while working with web-based dashboards.Bokeh can be easily used in conjunction with NumPy, Pandas, and other Python packages. It can be used to produce interactive plots, dashboards, and so on. It helps in communicating the quantitative insights to the audience effectively.Matplotlib and Seaborn produce static plots, whereas Bokeh produces interactive plots. This means when the user interacts with these plots, they change accordingly.Plots can be embedded as output of Flask ... Read More

Time series, as the name suggests, is data that contains certain time periods or time stamps. It contains observations over certain time period. This kind of data tells us about how variables change over time based on various factors. Time series analysing and forecasting can be used to predict data with respect to some future time.Univariate time series contains values taken with respect to a single variable at certain time instances over a period of time. A multivariate time series contains values taken with respect to multiple variables at same periodic instances of time.Time series consists of 4 components that ... Read More

Scikit−learn, commonly known as sklearn is a library in Python that is used for the purpose of implementing machine learning algorithms. It is an open-source library hence it can be used free of cost. It is powerful and robust, since it provides a wide variety of tools to perform statistical modelling. This includes classification, regression, clustering, dimensionality reduction, and much more with the help of a powerful, and stable interface in Python. The library is built on Numpy, SciPy and Matplotlib libraries.It can be installed using the ‘pip’ command as shown below −pip install scikit−learnThis library focuses on data modelling. ... Read More

Scikit−learn, commonly known as sklearn is a library in Python that is used for the purpose of implementing machine learning algorithms. It is an open−source library hence it can be used free of cost.It is powerful and robust, since it provides a wide variety of tools to perform statistical modelling. This includes classification, regression, clustering, dimensionality reduction, and much more with the help of a powerful, and stable interface in Python.This library is built on Numpy, SciPy and Matplotlib libraries.It can be installed using the ‘pip’ command as shown below −pip install scikit−learnThis library focuses on data modelling.The streamlining operation ... 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