Machine Learning

Deep learning is a branch of  machine learning   which is completely based on   artificial neural networks , as the neural network is going to mimic the human brain so deep learning is also a kind of mimic of the human brain. In deep learning, we don’t need to explicitly program everything. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture. A formal definition of deep learning is- neurons "Deep learning is a particular kind of machine learning that achieves great  power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones". In the human brain approximatel

Gradient descent variants There are three variants of gradient descent, which differ in how much data we use to compute the gradient of the objective function. Depending on the amount of data, we make a trade-off between the accuracy of the parameter update and the time it takes to perform an update. Gradient descent variants Batch gradient descent Stochastic gradient descent Mini-batch gradient descent

The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural network is usually a computational network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons that are linked to each other in various layers of the networks. These neurons are known as nodes. Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building blocks, unsupervised learning, Genetic algorithm, etc. What is Artificial Neural Network? The term " Artificial Neural Network " is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one

  An ensemble is a machine learning model that combines the predictions from two or more models.  The models that contribute to the ensemble, referred to as ensemble members, maybe the same type or different types and may or may not be trained on the same training data.  The predictions made by the ensemble members may be combined using statistics, such as the mode or mean, or by more sophisticated methods that learn how much to trust each member and under what conditions. The study of ensemble methods really picked up in the 1990s, and that decade was when papers on the most popular and widely used methods were published, such as core bagging, boosting, and stacking methods. Why should we consider using an ensemble? There are two main reasons to use an ensemble over a single model, and they are related; they are: Performance:  An ensemble can make better predictions and achieve better performance than any single contributing model. Robustness : An ensemble reduces the spread or disper

  Principal Component Analysis is an unsupervised learning algorithm that is used for dimensionality reduction in   machine learning . It is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation. These new transformed features are called the   Principal Components . It is one of the popular tools that is used for exploratory data analysis and predictive modelling. It is a technique to draw strong patterns from the given dataset by reducing the variances. PCA generally tries to find the lower-dimensional surface to project the high-dimensional data. PCA works by considering the variance of each attribute because the high attribute shows the good split between the classes, and hence it reduces the dimensionality. Some real-world applications of PCA are  image processing, movie recommendation system, optimizing the power allocation in various communication channels.  It is a featu

“Support Vector Machine” (SVM) is a supervised  machine learning algorithm   that can be used for both classification or regression challenges. However,  it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is a number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot). Support Vectors are simply the coordinates of individual observation. The SVM classifier is a frontier that best segregates the two classes (hyper-plane/ line).

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.  Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled outcomes. A cluster refers to a collection of data points aggregated together because of certain similarities. How the K-means algorithm works To process the learning data, the K-means algorithm in data mining starts with the first group of randomly selected centroids, which are used as the beginning points for every cluster, and then performs iterative (repetitive) calculations to optimize the positions of the centroids It halts creating and optimizing clusters when either: The centroids have stabilized — there is no change in their values because the clustering has been successful. The defined number of iterations has been achieved.