The MachineLearning EngineerIssue #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 🚀.

Support the ML Engineer!

Forward the email, or share the online version on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute!

Domain knowledge in DL

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 in Tensorflow

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.

Live ggplots for your twitter rants

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).

Evolving Jupyter in the Lab

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.

8 Books for Computer Vision

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.

Qrash Course on RL

“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 — 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 “Agent”) 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.

Introducing data labelling section

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.

MLOps = Featured OS Libraries

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!

MLConf = Conferences & Events

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.

• PyCon + PyData Florence [02/05/2019] - Python X comes this year with a PyData focus in Florence, Italy.

• AI Conference Beijing [18/06/2019] - O’Reilly’s signature applied AI conference in Asia in Beijing, China.

• RAAIS 2019 [28/06/2019] - The Research and Applied AI Summit in London, UK

• 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

• AI Expo Global [19/04/2019] - Global conference on artificial intelligence in London, UK.

• Come join us at our talk on AI orchestration at scale.

• Predictive Analytics World [18/11/2019] - Conference for Business AI in Berlin, Germany.

• Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.

MLJobs = Jobs & Careers

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

• Seldon is hiring for a Machine Learning / Data Engineer in London
• Migacore is hiring for a Machine Learning Engineer in London
• CloudNC is hiring for a Machine Learning Engineer in London
• Babylon Health is hiring for a Machine Learning Engineer in London
• Chattermill is hiring for a Machine Learning Engineer in London

Mid-level Opportunities

• Proportunity is hiring for a Senior Machine Learning Engineer in London
• Twitter is hiring for a Senior Machine Learning Engineer in London
• Atlas ML is hiring for a Lead NLP Engineer in London
• StreetBees is hiring for a Senior Data Scientist in London
• Expedia is hiring for a Principal Data Scientist in London
• QuantumBlack is hiring for a Senior Machine Learning Engineer in London

Leadership Opportunities

• Fractal Labs is hiring for a VP of Engineering in London
• Distributed is hiring for a VP of Engineering in London
• FactMata is hiring for a Head of Machine Learning in London
• Brainpool.ai is hiring for a Head of Machine Learning in London, UK
• Cytora is hiring for a Data Science Director in London