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🤖 MLguru #15: The State of ML Frameworks, Machine Translation, and PyTorch 1.3

Welcome to the 15th edition of MLguru, a bi-weekly Machine Learning newsletter. In this edition you will read about:

  • The State of Machine Learning Frameworks in 2019,
  • Machine Translation by Facebook,
  • PyTorch 1.3,
  • And much more!

We also still have few places for our face-to-face Machine Learning workshops that will take place in Dublin. Click here for more details.

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Recent Advances in Low-Resource Machine Translation

Machine translation (MT) is one of the most successful applications of natural language processing (NLP) today, outperforming human-level performance in some language translation tasks. Unfortunately, the current high-performing MT systems need large-scale parallel data sets to fit hundreds of millions of model parameters. Most languages today don’t have resources to support this process, and there is a need need to build systems that can work effectively for everyone.

To solve this issue, Facebook AI team have developed a novel approach that combines several methods, including iterative back-translation and self-training with noisy channel decoding, which can tackle this problem. Read more


A Visual Analysis Tool to Explore Learned Representations in Transformers Models

The new version of BERT is here! exBERT is an interactive tool that provides insights into the meaning of the contextual representations by matchinghuman-specified input to similar contexts in a large annotated dataset. By aggregating the annotations of the matching similar contexts, exBERT helps to give an intuitive explanation of what each attention-head has learned. Read more


The State of Machine Learning Frameworks in 2019

The Gradient has just published the State of Machine Learning Frameworks in 2019, which answers the question about the leader. Is it PyTorch or maybe TensorFlow? Check the article to get the answers.


PyTorch 1.3 adds mobile, privacy, quantization, and named tensors

PyTorch continues to work on meeting the needs of its users. It has received enthusiastic support from the AI community, and its citations in papers on ArXiv grew by 194 percent in the first half of 2019 alone. Facebook, Microsoft, and Uber are increasingly using it as the foundation for their most important machine learning research and production workloads.

The release of PyTorch 1.3 includes experimental support for features such as seamless model deployment to mobile devices, model quantization for better performance at inference time, and few front-end improvements. Learn more about all the new functionalities on the official PyTorch website.

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Hydra: A framework that simplifies the development of complex applications

Hydra is a lightweight framework that simplifies the development of Python applications, especially complex ones, by introducing the ability to compose and override configurations. Hydra’s flexible approach to developing, creating, and maintaining code and configurations can help speed the development of complex applications in various fields, including machine learning research. Read Facebook’s article about all it’s features.


Smart Display – book your room in seconds

Last but not least, I'd like to show you our last project, Smart Display. It’s a tool that allows you to book a room in just a few seconds. Check the video below to know the details.

If you’d like to know more about the project, check Smart Display’s case study.

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