- How to fast-track machine translation projects - Nov 16, 2021.
Data is the lifeblood of any successful machine learning model, and machine translation models are no exception. Without relevant and properly labelled data, even the most sophisticated model will be unable to achieve reliable results.
- Machine Translation in a Nutshell - May 17, 2021.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.
- Attention mechanism in Deep Learning, Explained - Jan 11, 2021.
Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.
- Natural Language Processing Q&A - Jun 24, 2019.
In this Q&A, Jos Martin, Senior Engineering Manager at MathWorks, discusses recent NLP developments and the applications that are benefitting from the technology.
- My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
- 10 Exciting Ideas of 2018 in NLP - Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
- Free resources to learn Natural Language Processing - Sep 18, 2018.
An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.
- Machine Learning Translation and the Google Translate Algorithm - Sep 14, 2017.
Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.
- Attention and Memory in Deep Learning and NLP - Jan 12, 2016.
An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.
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