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Showing posts from August, 2019

The basic principle of robotics and AI

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Artificial intelligence applied to robotics development requires a different set of skills from you, the robot designer or developer. You may have made robots before. You probably have a quadcopter or a 3D printer. The familiar world of  Proportional Integral Derivative  ( PID ) controllers, sensor loops, and state machines must give way to artificial neural networks, expert systems, genetic algorithms, and searching path planners. We want a robot that does not just react to its environment as a reflex action, but has goals and intent—and can learn and adapt to the environment. We want to solve problems that would be intractable or impossible otherwise. Robotics or a robotics approach to AI—that is, is the focused learning about robotics or learning about AI? about how to apply AI tools to robotics problems, and thus is primarily an AI using robotics as an example. The tools and techniques learned will have applicability even if you don’t do robotics, but just apply AI to
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Introduction to Asynchronous Programming in .NET

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Asynchronous programming is generally one of the hardest programming concepts to grasp. It is even harder to understand what it means when it comes to dealing with server applications, notably web applications. Besides the general concept, the asynchronous programming model in the .NET Framework was also hard to grasp in the early days of the framework. With the introduction of Task Parallel Library (TPL) in .NET version 4.0, it became easier, but the programming model was still not ideal. With the new asynchronous programming language features of C# in .NET 4.5, it got easier still. It now may be said to offer the ideal programming model as well. In terms of both infrastructure and extensibility points, ASP.NET Web API framework is asynchronous from top to bottom by design. Also, it leverages the Task-based Asynchronous Pattern (or TAP) introduced in .NET 4.0 to enable this. As we will be dealing with asynchrony throughout the book, it would be helpful to ensure upfront that
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What Would a Next-Gen Data Scientist Do?

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   Next-gen data scientists don’t let money blind them to the point that their models are used for unethical purposes. They seek out opportunities to solve problems of social value and they try to consider the consequences of their models. Finally, there are ways to do good: volunteer to work on a long-term project (more than a hackathon weekend) with DataKind . There are also ways to be transparent: Victoria Stodden is working on RunMyCode , which is all about making research open-source and replicable. We won't step aside for a moment and let someone else highlight how important we think ethics—and vanquishing hubris—are to data sci‐ once. Professor Matthew Jones, from Columbia’s History Department, attended the course. He is an expert in the history of science, and he wrote up some of his thoughts based on the course. We’ve included them here as some very chewy food for thought avoiding rote activity. Importance of Data science Computational statistics a
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Data Scientist Interview Question

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If you want to Become data scientist you have must know these questions. A data scientist should not only be evaluated only on his/her knowledge on machine learning, but he/she should also have good expertise in statistics. Prepare from onlineitguru is providing data science interview questions Explain The Various Benefits Of R Language? The R programming language includes a set of a software suite that is used for graphical representation, statistical computing, data manipulation, and calculation.      what is data science? Data science is a "concept to unify statistics, data analysis, machine learning, and their related methods" to "understand and analyze actual phenomena" with data. Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.         What is selection Bias? Selection bias occurs when the sample obtained is not representative of the population intended t
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