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Neural machine translation Introduction This post is a collection of best practices for using neural networks in Natural Language Processing. It will be updated periodically as new insights become available and in order to keep track of our evolving understanding of Deep Learning for NLP.
While this has been true over the course of the last two years, the NLP community is slowly moving away from this now standard baseline and towards more interesting models. However, we as a community do not want to spend the next two years independently re- discovering the next LSTM with attention.
We do not want to reinvent tricks or methods that have already been shown to work. While many existing Deep Learning libraries already encode best practices for working with neural networks in general, such as The objectives of deep learning schemes, many other details, particularly task or domain-specific considerations, are left to the practitioner.
This post is not meant to keep track of the state-of-the-art, but rather to collect best practices that are relevant for a wide range of tasks. In other words, rather than describing one particular architecture, this post aims to collect the features that underly successful architectures.
While many of these features will be most useful for pushing the state-of-the-art, I hope that wider knowledge of them will lead to stronger evaluations, more meaningful comparison to baselines, and inspiration by shaping our intuition of what works. I assume you are familiar with neural networks as applied to NLP if not, I recommend Yoav Goldberg's excellent primer  and are interested in NLP in general or in a particular task.
|Crafting Deep Learning Objective Functions now Obsolete||History[ edit ] Although named after Bloom, the publication of Taxonomy of Educational Objectives followed a series of conferences from towhich were designed to improve communication between educators on the design of curricula and examinations. Cognitive  was published inand in the second volume Handbook II:|
|An Introduction to Deep Learning for Generative Models||Dataiku provides a plugin that supplies a number of pre-trained deep learning models that you can use to classify images.|
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The main goal of this article is to get you up to speed with the relevant best practices so you can make meaningful contributions as soon as possible.
I will first give an overview of best practices that are relevant for most tasks. I will then outline practices that are relevant for the most common tasks, in particular classification, sequence labelling, natural language generation, and neural machine translation.
Treating something as best practice is notoriously difficult: Best according to what? What if there are better alternatives? This post is based on my necessarily incomplete understanding and experience. In the following, I will only discuss practices that have been reported to be beneficial independently by at least two different groups.
I will try to give at least two references for each best practice. Best practices Word embeddings Word embeddings are arguably the most widely known best practice in the recent history of NLP. It is well-known that using pre-trained embeddings helps Kim, .
The optimal dimensionality of word embeddings is mostly task-dependent: Depth While we will not reach the depths of computer vision for a while, neural networks in NLP have become progressively deeper. Models for some tasks can be even deeper, cf.
Google's NMT model with 8 encoder and 8 decoder layers Wu et al. These observations hold for most sequence tagging and structured prediction problems. For classification, deep or very deep models perform well only with character-level input and shallow word-level models are still the state-of-the-art Zhang et al.
Layer connections For training deep neural networks, some tricks are essential to avoid the vanishing gradient problem. Different layers and connections have been proposed. Here, we will discuss three: As we can see, highway layers are similar to the gates of an LSTM in that they adaptively carry some dimensions of the input directly to the output.
Highway layers have been used pre-dominantly to achieve state-of-the-art results for language modelling Kim et al. Sristava's page contains more information and code regarding highway layers. Residual connections are even more straightforward than highway layers and learn the following function: This simple modification mitigates the vanishing gradient problem, as the model can default to using the identity function if the layer is not beneficial.
Dense connections then feed the concatenated output from all previous layers as input to the current layer: Dense connections have been successfully used in computer vision.
Dropout While batch normalisation in computer vision has made other regularizers obsolete in most applications, dropout Srivasta et al. A dropout rate of 0.The Project Team. This initiative brings together the expertise of Action contre la Faim (ACF), the London School of Hygiene and Tropical Medicine (LSHTM), and CAWST (Centre for Affordable Water and Sanitation Technology)..
ACF are at the forefront of Water, . Yesterday I started writing a blog post in response to several thoughts/questions that I’ve heard mumbled around me.
I addressed the first of these (see below) in some detail and would like to thank all of the practising teachers out there who supported me and our profession with their responses on Twitter, Facebook and the post itself.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems TensorFlow is an interface for expressing machine learning algorithms, and an . Author of Artificial Intuition and the Deep Learning Playbook — Intuition Machine Inc. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms.
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