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In Agile development, User Stories are the method for capturing needs. These User Stories are a concise description of functionality that a user requires from a system. User Stories must fit into one sprint. In this article, we will explore why User Stories need to fit into one sprint. Definition of User Story A User Story is a short, simple description of a feature told from the user's perspective. The User Story captures what the user wants to achieve and why. It is a placeholder for a conversation between the development team and the user. Importance of User Story Fitting ... Read More
We all know that a badly managed endeavour can result in missed deadlines, slower product development, staff stress, loss of critical personnel, and team infighting. Agile transformation modifies how businesses operate and behave, which contributes to change. In this article we will discuss more about agile transformation, advantages of it and finally why employees need to transform them to Agile. Let’s start! Agile Transformation When a company fully adopts an agile strategy, it is said to have undergone an agile transformation. The Agile Manifesto's values and principles must be embraced by every person working in every department for a firm to ... Read More
When we talk about agile software development, Scrum and Kanban are two widespread frameworks. Scrum is an iterative & incremental framework for product development management, while Kanban is a pull-based system for managing work. Scrum-ban is a hybrid of these two frameworks. It incorporates the principles & practices of both Scrum and Kanban. Scrum-ban aims to supply the best of both worlds by incorporating Scrum's focus on teamwork, collaboration & continuous progress with Kanban's emphasis on visualizing work, managing flow & limiting work in progress. Scrumban Principles Continuous improvement − Scrum-ban promotes continuous progress by enabling teams to reflect ... Read More
Introduction The rapidly expanding fields of artificial intelligence and machine learning are to thank for our machines' increasing intelligence and independence. But both fields are extremely complicated, and getting a greater understanding of them requires time and effort. The methods for regression and classification, which both predict in machine learning and employ labeled datasets, are called supervised learning algorithms. However, their point of departure is how they approach Machine Learning problems differently. Let's now examine Regression vs. Classification in greater detail. This article examines the definitions, kinds, distinctions, and application cases of regression vs. classification in machine learning. Regression ... Read More
Introduction We might picture sci-fi-inspired, humanoid automatons when we think about robots. Numerous different types of robots are in use today, even though these are still largely imagined. How will they transform the world, exactly? Robots are devices that possess the ability to sense, think, plan, and act independently. They can mimic human behavior and extend human capabilities and accomplish tasks independently. The word robot comes from the Czech word robota, which denotes forced labor. This article will examine the world of robots and how they may affect the future. Main Frame About Robotics The science of building ... Read More
Introduction The definition of manual is evolving in a world when almost all manual operations are mechanized. There are many different kinds of machine learning algorithms available today, some of which can help computers learn, get smarter, and resemble humans more. Because technology is advancing rapidly right now, it is possible to anticipate the future by looking at how computers have changed over time. Many different machine learning algorithms have been developed in these extremely dynamic times to aid in resolving difficult real-world problems. The automated, selfcorrecting ML algorithms will get better over time. Let's look at the various sorts ... Read More
Introduction Data encodings are unsupervised learned using an artificial neural network called an autoencoder. An autoencoder learns a lower-dimensional form (encoding) for a higher-dimensional data to learn a higher-dimensional data in a lower-dimensional form, frequently for dimensionality reduction. Autoencoders Autoencoders are very useful in the field of unsupervised machine learning. They can be used to reduce the data's size and compress it. Principle Component Analysis (PCA), which finds the directions along which data can be extrapolated with the least amount of variance, and autoencoders, which reconstruct our original input from a compressed version of it, differ from one another. If ... Read More
Introduction Kohonen proposed the idea of a self-organizing map (SOM) in the first place. Since it is an unsupervised neural network that is trained using unsupervised learning methods to create a low-dimensional, discretized representation from the input space of the training samples, it is a way to minimise the dimensions of the data. A map is a common name for this representation. This article will walk through a Kohonen Map beginner's guide, which is a well-known self-organizing map. To begin, let's define what self-organizing maps are. Self-Organizing Maps Self-organizing maps, also known as Kohonen maps or SOMs, are ... Read More
Introduction Machine learning models are essential for successful AI implementation since they represent the mathematical underpinnings of artificial intelligence. The quality of our AI depends entirely on the machine models that power it. We require a method for objectively evaluating our machine learning model's performance and deciding whether it is suitable for use. If we had a ROC curve, that would be useful. Everything that we need to learn about ROC curves is covered in this article. ROC Curve A Receiver Operating Characteristic (ROC) curve is a graphical representation of the performance of a binary classification model. It plots ... Read More
Introduction The learning component of artificial intelligence (AI) is indeed the focus of the area of machine learning. Algorithms that represent a set of data are used to create this learning component. To train machine learning models, certain datasets are sent through the algorithm. This article will define the term "Epoch, " which is used in machine learning, as well as other related topics like iterations, stochastic gradient descent. Anyone studying deep learning and machine learning or attempting to pursue a career in this industry must be familiar with these terms. Epoch in ML In machine learning, an epoch is ... Read More