Задайте вопросы, которые Вы хотели бы обсудить на ближайшем семинаре.

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Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments)


Translations: Chinese (Simplified), French, Japanese, Korean, Russian, Spanish

Watch: MIT’s Deep Learning State of the Art lecture referencing this post

In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformers outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions.

The Transformer was proposed in the paper Attention is…

Materials for deep NLP course. Contribute to deepmipt/deep-nlp-seminars development by creating an account on GitHub.

Designing the right file architecture is not straightforward in Machine Learning. After struggling on that question for a few projects of…

NB: Please go to http://course.fast.ai to view this video since there is important updated information there. If you have questions, use the forums at http://forums.fast.ai

We complete our work from the previous lesson on tabular/structured, time-series data, and learn about how to avoid overfitting by using dropout regularization. We then introduce natural language processing with recurrent neural networks, and start work on a language model.

Posted by Jacob Devlin and Ming-Wei Chang, Research Scientists, Google AI Language   One of the biggest challenges in natural language proce...

An approachable and understandable explanation of BERT, a recent paper by Google that achieved SOTA results in wide variety of NLP tasks.

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Hacker News (98 points, 19 comments), Reddit r/MachineLearning (164 points, 20 comments)


Translations: Chinese (Simplified), French, Japanese, Korean, Persian, Russian

The year 2018 has been an inflection point for machine learning models handling text (or more accurately, Natural Language Processing or NLP for short). Our conceptual understanding of how best to represent words and sentences in a way that best captures underlying meanings and relationships is rapidly evolving. Moreover, the NLP community has been putting forward incredibly powerful components that you can freely download and use in your own models and pipelines (It’s been referred to as NLP’s ImageNet moment, referencing how years ago similar developments accelerated the development of machine learning in Computer Vision tasks).

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Остроухов,Петр,Алексеевич,5,3,4,4,5,4,4,5,10,7,8,6,10,5,7,50,125,3,8d430f0126399382df26e69288eb8323
Сафин,Камиль,5,5,5,5,4,5,5,9...

Welcome Back to the NeurIPS 2018 Tutorial Sessions. This tutorial Unsupervised Deep Learning will cover in detail, the approach to simply 'predict everything' in the data, typically with a...