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التعلّم العميق
1963
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Created by
Shadi Saleh
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هذا الكتاب تم وضعه من قبل ثلاثة خبراء في المجال, وهو الكتاب الوحيد الذي يشرح تفاصيل واضحة في هذا الموضوع - ايلون موسك
Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; cof-ounder and CEO of Tesla and SpaceX
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نعرض في هذا العمل جهودنا في بناء نظام ترجمة باستخدام تقنية التعلم العميق ، ونبين الطريقة التي اتبعناها في تطوير النموذج ، وإضاقة طرق تفيد في تحسين دقة الترجمة من اللغة الإنكليزية إلى اللغة العربية.
Deep learning is at the heart of the current rise of artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have
demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently lead to a large influx of contributions in this direction. This article presents a survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them
We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and meta-data of the corresponding document streams. We focus on learning an embedding that maps variable-sized s
amples of user activity--ranging from single posts to entire months of activity--to a vector space, where samples by the same author map to nearby points. Our approach does not require human-annotated data for training purposes, which allows us to leverage large amounts of social media content. The proposed model outperforms several competitive baselines under a novel evaluation framework modeled after established recognition benchmarks in other domains. Our method achieves high linking accuracy, even with small samples from accounts not seen at training time, a prerequisite for practical applications of the proposed linking framework.
Active learning has been shown to reduce annotation requirements for numerous natural language processing tasks, including semantic role labeling (SRL). SRL involves labeling argument spans for potentially multiple predicates in a sentence, which mak
es it challenging to aggregate the numerous decisions into a single score for determining new instances to annotate. In this paper, we apply two ways of aggregating scores across multiple predicates in order to choose query sentences with two methods of estimating model certainty: using the neural network's outputs and using dropout-based Bayesian Active Learning by Disagreement. We compare these methods with three passive baselines --- random sentence selection, random whole-document selection, and selecting sentences with the most predicates --- and analyse the effect these strategies have on the learning curve with respect to reducing the number of annotated sentences and predicates to achieve high performance.
The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information. Since the 2016 US presidential election, the term fake news'' became increasingly popular and thi
s phenomenon has received more attention. In the past years several fact-checking agencies were created, but due to the great number of daily posts on social media, manual checking is insufficient. Currently, there is a pressing need for automatic fake news detection tools, either to assist manual fact-checkers or to operate as standalone tools. There are several projects underway on this topic, but most of them focus on English. This research-in-progress paper discusses the employment of deep learning methods, and the development of a tool, for detecting false news in Portuguese. As a first step we shall compare well-established architectures that were tested in other languages and analyse their performance on our Portuguese data. Based on the preliminary results of these classifiers, we shall choose a deep learning model or combine several deep learning models which hold promise to enhance the performance of our fake news detection system.
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