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Issue #35
This week in Issue #35:
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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!
Great insight into the near future of data engineering covering the current transformations that this role has been undergoing, as well as some of the trends that will become more prominent in the immediate and medium term for data engineers. The article covers the transition from batch into realtime, the exponential increase of connectivity, automation, descentralisation and beyond.
Former Apple engineer shares his experience transitioning into a machine learning full time role. In this article he shares 6 techniques that helped him study machine learning 5 days a week. This includes reducing search space, fixing your environment, setting up your system, work smart, embrace being stuck and the 3-year old principle.
If you are interested in NLP, KDNuggets has put together an excellent list of 12 NLP researchers to follow to stay up to date with some of the latest cutting edge tools and research in this field. The list includes some really great practitioners and researchers, including Explosion (and SpaCy) cofounders Matt Honibal & Ines Montani, DeepMind Researcher Sebastian Ruder, FastAI founder Jeremy Howard and more.
N-Shot Learning is very exciting area of research which focuses on tackling challenges with "Small Data". That is, using n-data examples. These can be from zero-shot learning - zero examples - to 1-short, to few-shot, etc. This is an interesting challenge as it requires a lot of the domain expertise and in an abstract sense the concept of intuition to be embedded in the algorithms to be able to abstract insights that would otherwise be missed by vanilla deep learning (or even traditional machine learning) algorithms.
Excellent piece by Twitter's Ferenc Huszár on Counterfactuals. Counterfactuals is an incredibly interesting technique that falls within the causal inference family. This technique has been growing in popularity especially due to its intuitive explanatory power. From a high level definition, a counterfactual asks the question around "what would be the minimum change that I could make to change the outcome". In machine learning this has great predictive power.
OSS: NN Architecture AutoML
The theme for this week's featured ML libraries is Neural Network Architecture AutoML, which you can find in our Production Machine Learning ecosystem list. These libraries are an incredibly exciting addition that fall in our Responsible ML Principle #4. The four featured libraries this week are:
  • Neural Network Intelligence - NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
  • Autokeras - AutoML library for Keras based on "Auto-Keras: Efficient Neural Architecture Search with Network Morphism".
  • ENAS-PyTorch - Efficient Neural Architecture Search (ENAS) in PyTorch based on this paper.
  • Neural Architecture Search with Controller RNN - Basic implementation of Controller RNN from Neural Architecture Search with Reinforcement Learning and Learning Transferable Architectures for Scalable Image Recognition.
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
  • 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.
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
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