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Issue #24
 This week in Issue #24:
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
A great time to be alive thanks to the incredible e-learning resources. Standford has made online their computer science course on Deep Learning for Natural Language Processing. All the video lectures can be found online for free - a great end-to-end introduction to the theory and practice of several cutting edge concepts. Many few alternative resources are available as well, such as Deep Mind's deep learning NLP course which can be found on Github.
Alluxio is an open source framework that provides and advocates for a data orchestration layer. This basically includes an architectural layer that is in charge of simplifying and standardising data access, making it easier for data scientists and engineers to load and interact with the right datasets. With datasets and data sources growing exponentially, this opportunity will only grow - Alluxio provides a really interesting whitepaper where they explain these challenges, and cover some of the benefits that a platform like alluxio can bring to the table.
Last week we shared Jay's work on Attention in Seq2seq NLP models. This week Jay comes back with another great visual deep dive into the transformer - a model that uses attention to speed the speed in which these models can be trained.
Google released a "People+AI" guidebook where they have made available a great and extensible resource that introduces fundamental knowledge for designing human-centered AI products. The guide covers an overview of machine learning and automation, as well as more high level (and critical) topics such as data collection, explainability, trust, feedback, control, erros, feedback and more.
The space on machine learning reproducibility keeps surprising us with a lot of innovative approaches - this week Cecelia Shao from CometML has put together a tutorial on how to build a reproducible machine learning pipeline using Comet.ML and Quilt. In this tutorial she shows us how we can build a Keras image classifier on a fruits dataset.
This week we have seen yet another great piece of research by the Samsung AI team which has also brought a video how they are able to use this tech to bring world famous paintings (like the Mona Lisa) to life. For anyone interested to dive deeper into the world of GANs, there is a Manning book "GANs in Action" by Jakub Langr which has made available content for free.
MLOps = Featured OS Libraries
We are excited to see the Awesome MLOps list growing to almost 600 stars now! Thanks to everyone for your support! This week's edition is focused on industrial strength visualisation frameworks which fall on our Responsible ML Principle #5. The four featured libraries this week are:
  • SpaCy - Industrial-strength natural language processing library built with python and cython by the team.
  • Flair - Simple framework for state-of-the-art NLP developed by Zalando which builds directly on PyTorch.
  • Wav2Letter++ - A speech to text system developed by Facebook's FAIR teams.
  • Gensim - A python library that focuses on topic modelling, document indexing and similarity retrieval
If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request
We feature conferences that have core  ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up.
Technical & Scientific Conferences
  • AI Conference Beijing [18/06/2019] - O'Reilly's signature applied AI conference in Asia in Beijing, China.
  • Data Natives [21/11/2019] - Data conference in Berlin, Germany.
  • ODSC Europe [19/11/2019] - The Open Data Science Conference in  London, UK.
Business Conferences
  • Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.
We showcase Machine Learning Engineering jobs (primarily in London for now) to help our community stay up to date with great opportunities that come up. It seems that the demand for data scientists continues to rise!
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
© 2018 The Institute for Ethical AI & Machine Learning