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NLP News - 2017 Year in Review, 2018 Prognoses, Semi-supervised learning, CTC networks, random forests tutorials, super-human SQuAD, M is Dead, Advances in Pre-training Word Embeddings

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Happy New Year to you all! This edition looks back at the past year with the best reviews of 2017 and
 
January 15 · Issue #14 · View online
NLP News
Happy New Year to you all! This edition looks back at the past year with the best reviews of 2017 and ahead to 2018. We also have some exciting tutorials on semi-supervised image classification, CTC networks, random forests, and giving a captivating scientific presentation. As always, there are more interesting blog posts, industry highlights, and exciting papers. Enjoy!

2017 Year in Review
WildML's AI and Deep Learning in 2017
Best of ML 2017 Reddit Survey Results
ML/NLP Publications in 2017
Review of my 2017 Forecasts
30 Amazing Machine Learning Projects for the Past Year
Research Blog: The Google Brain Team — Looking Back on 2017
Domain adaptation highlights in 2017
... And Looking Ahead to 2018
Artificial Intelligence, AI in 2018 and beyond
Five Jobs That Are Set to Grow in 2018
Tools and implementations
wav2letter: Facebook AI Research Automatic Speech Recognition Toolkit
Tutorials
Semi-supervised image classification explained
CTC Networks and Language Models: Prefix Beam Search Explained
The David Attenborough Style of Scientific Presentation
Identifying churn drivers with Random Forests
More blog posts and articles
Five Trends to Avoid When Founding a Startup
Your Tweets Could Show If You Need Help for Bipolar Disorder
First super-human model on SQuAD
Industry insights
Facebook’s Virtual Assistant M Is Dead. So Are Chatbots
COTA: Improving Uber Customer Care with NLP & Machine Learning
Having A Heart Attack? This AI Helps Emergency Dispatchers Find Out
Unbabel raises $23 million Series B
Paper picks
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Advances in Pre-Training Distributed Word Representations
A Flexible Approach to Automated RNN Architecture Generation
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