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Issue #14
This week in Issue #14:
The power of domain knowledge in deep learning, federated learning in tensorflow, supercharging your twitter rants with charts, Jupyter notebook evolution, top books on computer vision, crash course on deep Q nets, data labelling frameworks, upcoming AI conferences, new Machine Learning jobs and more πŸš€.
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Deep neural networks are great at learning complex patterns, however one of the most powerful methods that is often overlooked is introducing a-priori knowledge into your deep learning pipeline to simplify and even improve the performance of your models. This great presentation by CMU Professor Russ Salakhutdinov dives into this topic and provides an insight of both the technical, scientific and human dimensions to this topic. The slides are available online, as well as the video of his presentation
Federated learning comes back this week, the method which allows for machine learning models to be trained across multiple edge devices in the network instead on a central server. A few weeks back we shared a tutorial on how to convert your PyTorch ML pipeline in to a federated one - this week the tensorflow team brings us an insight of a federated learning example with tensorflow using the good old MINST dataset. An exciting technique that can allow us to put the 3 billion smartphones in the world and 7 billion connected devices to work whilst having the potential to truly respect privacy.
Supercharge your twitter interactions using the great tool chain provided in this article to add live and interactive plots in your tweets and beyond. This tutorial shows you how to create this tweetable piece by using ggplot2, R's plotly R package and a few other tools. To add extra points your can also use interesting themes such as the XKCD plot themes (yes, they are actually a thing).
Great article covering an important piece of technology that has been leading the way in various areas of machine learning - Jupyter Labs. The article covers an overview of JupyterLabs, together with a 101 of how to install and interact with this fully fledged IDE. If you are interested on learning about more Notebook-like projects check out the data science notebooks section in our machine learning operations list.
Machine learning mastery brings us a comprehensible list of great books to get started into the broad and deep world of computer vision. This great article provides a list of Jason's top 5 computer vision textbooks as well as his top 3 computer vision programmer books. If you need further motivation to look into this field, there is also a great post that covers 9 applications of deep learning for computer vision.
"Out of all the different types of Machine Learning fields, the one fascinating me the most is Reinforcement Learning. For those who are less familiar with it β€” while Supervised Learning deals with predicting values or classes based on labeled data and Unsupervised Learning deals with clustering and finding relations in unlabeled data, Reinforcement Learning deals with how some arbitrary being (formally referred to as an β€œAgent”) should act and behave in a given environment." Could not have been put in better words for motivations to read into this field, this article provides a great start by introducing a "Hello world" exercise with Deep Q Networks.
We are very excited to have a new addition in the Machine Learning Operations list on Data Labelling tools. Data labelling is one of the most challenging steps in machine learning projects, and often presents itself as a blocker. These open source tools aim to provide a solid base for teams and companies to introduce best practices in their data labelling process - some tools even providing functionality for team collaboration.
We are excited to see the Awesome MLOps list growing to over 300 stars now! Thanks to everyone for your support! This week's edition is focused on new libraries on Data Labelling Tools which fall on our Responsible ML Principle #2 and #6. The four featured libraries this week are:
  • Labelimg - Open source graphical image annotation tool writen in Python using QT for graphical interface focusing primarily on bounding boxes.
  • Computer Vision Annotation Tool (CVAT) - OpenCV's web-based annotation tool for both videos and images for computer algorithms.
  • Labelbox - Open source image labelling tool with support for semantic segmentation (brush & superpixels), bounding boxes and nested classifications.
  • Doccano - Open source text annotation tools for humans, providing functionality for sentiment analysis, named entity recognition, and machine translation.
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 Conferences
  • DataFest19 [11/03/2019] - Two week festival of Data Innovation hosted across Scotland, UK.
  • 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!
Junior Opportunities
Mid-level Opportunities
Leadership Opportunities
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