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Issue #143
This week in Issue #143:
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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 new online machine learning text book targetted for engineers and scientists being created from a recent online course, and which is covers a wide variety of resources including the underlying foundations, techniques, user aspects and matters related to ethics an responsibility in AI. Until the book is out the online course is available for free, which is yet another great resource that practitioners can benefit from.
This article aims to provide an intuitive introduction and overview of graph deep learning, and covering the concepts for implementing the internals as well as how it all integrates together.
Very interesting paper that discusses the motivations, challenges and solutions for massive-scale model training, and proposes a distributed training framework for giant models with the codename Whale.
For practitioners looking to brush up their data structures and algorithms, here is a github repo that contains a very comprehensive list of resources and material for typical algorithmic interview questions.
Similar to other "awesome" github lists, this "awesome kubernetes" list provides a set of useful resources for practitioners to dive into the world of kubernetes, and for intermediate practitioners to brush up their foundational skills.
The topic for this week's featured production machine learning libraries is GPU Acceleration Frameworks. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:
  • Vulkan Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced  data processing usecases.
  • CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
  • Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
  • CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
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
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
© 2018 The Institute for Ethical AI & Machine Learning