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Quick Tutorial for Numpy Linspace with Examples for Beginners

What is Linspace() in Numpy Numpy Linspace() function is used to create a numpy array with evenly spaced numbers between the two intervals provided as input.  In this tutorial, we will see the syntax of np.linspace() and see various examples by using various parameters. Numpy Linspace: np.linspace() Syntax np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0) start – This signifies the starting value of the sequence and can be a number or an array-like value. stop – This signifies the stop […]

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Tutorial of Violin Plot in Base R Language with Examples

Introduction In this tutorial, we will see how to create Violin Plot in Base R language which is very popular for providing packages for rich visualization. First, we will understand the syntax of vioplot() and then see how to use it for creating violin plot. Syntax of Violin Plot vioplot() function in R The basic syntax for vioplot() function is shown below with a few commonly used arguments. The detailed syntax can be found here. vioplot(x,horizontal,col,rectCol,lineCol,colMed,border) x – It denotes […]

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Tutorial of Line Plot in Base R Language with Examples

Introduction A line plot is a graph that connects multiple data points by making line segments between them. These points are ordered in one of their coordinate (usually the x-coordinate) values and are useful to investigate trends in our data set. In this tutorial, we are going to see how to create a line plot in R language with plot() function along with its syntax and various examples. Syntax plot() function for    

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Quick Tutorial for Python Numpy Arange Functions with Examples

What is Numpy Arange function in Python The Numpy Arange function is used to create a numpy array whose elements are evenly distributed within a given range.  In this tutorial, we will understand the syntax of np.arange() and go through multiple examples by using its various parameters. Numpy Arange : numpy.arange() Syntax numpy.arange(start=0, stop, step=1, dtype) start (optional) – It denotes the starting value of the range,    

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Agglomerative Hierarchical Clustering in Python Sklearn & Scipy

Introduction In this tutorial, we will see the implementation of Agglomerative Hierarchical Clustering in Python Sklearn and Scipy. First of all, we will understand in brief what is hierarchical clustering and how agglomerative clustering works. Then we will create our own sample dataset to show an example of creating a dendrogram graph in Scipy and implementing agglomerative clustering in sklearn. What is Hierarchical Clustering? Hierarchical clustering is an unsupervised clustering algorithm used to create clusters with a tree-like hierarchy. In […]

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YOLOv6 Explained with Tutorial and Example

Introduction In this article, we will introduce the new object detection model YOLOv6 which has been making buzz in the computer vision community ever since its GitHub was made public a few days back. We will take a brief look at its architecture and the improvement that its author claim. Then we will explain how to use YOLOv6 with step by step tutorial and example. What is YOLOv6? YOLOv6 is the object detection model created by a team at Meituan […]

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Split and Merge Image Color Space Channels in OpenCV and NumPy

Introduction In this tutorial, we will show you how to split the image into it’s multiple channels by using the OpenCV cv2.split() function and also with Numpy. We will also show you how we can merge the single channel images to generate the multi-channel image with the cv2.merge() function. Color Spaces and Channels The colors of images can be organized in many ways and this scheme is known as color space. For example, a popular way of representing the image […]

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Confidential Containers: Verifiably secure computation in the cloud

For many organizations, trusting their data to the cloud requires having a complete understanding of and control over the environment in which that data resides and how it’s being processed. Microsoft understands this, and we are committed to building a trustworthy cloud—one in which security, privacy, and transparency are built into its core. A key part of this vision is confidential computing—a set of hardware and software capabilities that give  

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AI4Science to empower the fifth paradigm of scientific discovery

Over the coming decade, deep learning looks set to have a transformational impact on the natural sciences. The consequences are potentially far-reaching and could dramatically improve our ability to model and predict natural phenomena over widely varying scales of space and time. Could this capability represent the dawn of a new paradigm of scientific discovery? Jim Gray, a Turing Award winner, and former Microsoft Technical Fellow, characterised the historical evolution of scientific discovery through 

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