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2018年机器学习:深刻的学习:使用人工智能安排照片——艾莎Owais-Sharjah天文学和空间科学中心

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生活在21世纪,人类? ? ?最引人注目的武器就是创新。我们热衷于创新的领域是软件工程,明确人工智能(AI)。名称提出,人工智能是绑在将产品转换为精明的经营者,他们看到活动依赖于地球。至于另外有适应性改变他们的目标他们打算做同样修改他们的活动依赖其进化的条件。使AI运营商非常规的是他们的能力,从他们的错误回忆。此外,机器学习(ML)是一种人工智能? ? ?年代授权框架的应用程序适应自然,提高通过理解和改变其活动没有人类的中介。这需要我们深入学习(DL),毫升担心的另一个领域被计算的结构和能力人吗? ? ?年代思想称为假冒神经系统。它有系统适合采取的信息从教会或无标号信息; consequently it is additionally known Deep Neural Network (DNN). Every one of those terms lead us to what we are for the most part keen on, Convolutional Neural Networks (CNNs), which is a profound neural system that is especially divider adjusted to arrange pictures, for our situation to group pictures of shooting stars.Inpainting is the process of reconstructing lost or deteriorated parts of images and videos. In the museum world, in the case of a valuable painting, this task would be carried out by a skilled art conservator or art restorer. In the digital world, inpainting refers to the application of sophisticated algorithms to replace lost or corrupted parts of the image data.This official definition of inpainting on Wikipedia already takes into account the use of “sophisticated algorithms” that do the same work of manually overwriting imperfections or repairing defects but in a fraction of the time.As deep learning technologies progress further, however, the process of inpainting has become automated in so complete a manner that these days, it requires no human intervention at all. Simply feed a damaged image to a neural network and receive the corrected output. Go ahead and try it out yourself, with NVIDIA’s web playground that demonstrates how their network fills in a missing portion for any image.Simply drag and drop any image file, erase a portion of it with the cursor and watch how the AI patches it up. I tried it on a few pictures lying around on my desktop. Here’s one of them below, with a big chunk of my face missing and the neural network restoring it again in a matter of seconds, albeit making me look like I just got out of a street fight.The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

传记:

艾莎Al欧维斯完成她修读计算机科学工程学院沙迦的美国大学。她作为一个研究助理填写在沙迦陨石中心天文学和空间科学中心。

艾莎Al欧维斯

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