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An Overview on Data Structures and Algorithm in Python

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Data structures and algorithms in Python are two of the most fundamental concepts in computer science. They are indispensable tools for any programmer. Data structures in Python deal with the organisation and storage of data in the memory while a program is processing it. On the other hand, Python Algorithm refer to the detailed set of instructions that helps in the processing of data for a specific purpose.  Alternately, it can be said that different data structures are logically utilised by algorithms to work out a particular problem of data analysis. Be it a real-world problem or a typical coding-related question, an understanding of data structures and algorithms in Python is crucial if you want to come up with an accurate solution. In this article, you will find a detailed discussion of different Python algorithms and data structures. Why use Python for algorithms? Python is a suitable programming language for learning about data structures and algorithms. For one, it’s excellent

All About Cloud Computing !!!

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  What is Cloud Computing ? Cloud computing refers to any kind of hosted service delivered over the internet. These services often include servers, databases, software, networks, analytics and other computing functions that can be operated through the cloud. Files and programs stored in the cloud can be accessed anywhere by users on the service, eliminating the need to always be near physical hardware. In the past, for example, user-created documents and spreadsheets had to be saved to a physical hard drive, USB drive or disk. Without some kind of hardware component, the files were completely inaccessible outside the computer they originated on. Thanks to the cloud, few people worry anymore about fried hard drives or lost or corrupted USB drives. Cloud computing makes the documents available everywhere because the data actually lives on a network of hosted servers that transmit data over the internet. Cloud computing refers to any kind of hosted service delivered over the internet. The

All About Computer Vision !!!

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What is Computer Vision? Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it. Technically, machines attempt to retrieve visual information, handle it, and interpret results through special software algorithms. Computer vision (CV) is a process (and a branch of computer science) that involves capturing, processing and analyzing real-world images and video to allow machines to extract meaningful, contextual information from the physical world. Today, computer vision is the foundation and a key means of testing and exploiting deep-learning models that are propelling the evolution of artificial intelligence toward ubiquitous, useful and practical applications. Here are a few common tasks that computer vision systems can be used fo

What is Machine Learning? How It Works And Is Useful ?

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What is Machine Learning? Machine learning (ML) equips computers to learn and interpret without being explicitly programmed to do so. Here, as the "computers", also referred as the "models", are exposed to sets of new data, they adapt independently and learn from earlier computations to interpret available data and identify hidden patterns. This involves data analysis and automation of analytical model-building using numerous ML algorithms. ML enables computers and computing machines to search for and identify hidden insights, without being programmed for where to look for, when exposed to new data sets. Although this technology is not new, it is now gaining fresh momentum as there are numerous things to know about ML. The factors responsible for resurging interest in ML are powerful and affordable computational processing, continuously growing volumes of huge data sets, and affordable data storage options. Today, companies can make informed decisions by using ML a

What Is Artificial Neural Network And How Is It Useful ?

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What is an Artificial Neural Network ? An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of Artificial Intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available. Human brains interpret the context of real-world situations in a way that computers can’t. Neural networks were first developed in the 1950s to address this issue. An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. ANNs are created by programming regular computers to behave as though they are interconnected brain cells. Artificial Neural Networks are the computational models that are inspired by the

How Artificial Intelligence Will Change The Future?

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Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data. Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. Artificial intelligence generally falls under two broad categories: Narrow AI: Sometimes referred to as "Weak AI," this kind of artificial intelligence operates wi