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March 16 · Issue #6 · View online
A weekly digest of all the best Data Science related news and blog posts.
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đ„đ„đ„Welcome to issue #6 of Best of Data Science Weeklyđ„đ„đ„ This week Iâm in Cape Town attending SatRday, Iâll include new content from the conference in future newsletters - http://capetown2018.satrdays.org. đđđ If youâd like to subscribe or browse any of the previous editions please use the link below: Drink up and enjoy this weeks installment.đ Have a great weekend and send me any questions or ideas for things youâd like to see in the newsletter.đ â Luis de Sousa
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R up to #12 in Redmonk language rankings
In the latest Redmonk language rankings, R has risen to the #12 position, up from #14 in the June 2017 rankings. Python remains steady in the #3 position. The Redmonk rankings are based on activity in StackOverflow (as a proxy for user engagement) and Github (as a proxy for developer engagement).
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Pixel-Level Land Cover Classification Using the Geo AI Data Science Virtual Machine and Batch AI
A tutorial illustrating how to create a deep neural network model that accepts an aerial image as input and returns a land cover label (forested, water, etc.) for every pixel in the image. Microsoftâs Cognitive Toolkit (CNTK) is used to train and evaluate the model on an Azure Geo AI Data Science Virtual Machine or an Azure Batch AI GPU cluster. The method shown was developed in collaboration between the Chesapeake Conservancy, ESRI, and Microsoft Research as part of Microsoftâs AI for Earth initiative.
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Comparing Deep Learning Frameworks: A Rosetta Stone Approach
Demo of running Neural Networks across different frameworks in Python. Link to GitHub.
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Demystifying Docker for Data Scientists â A Docker Tutorial for Your Deep Learning Projects | Machine Learning Blog
With Docker containers as the development environment for your deep learning projects, you can hit the ground running. You are spared the overhead of installing and setting up the environment for the various frameworks and can start working on your deep learning projects right away. Scripts are guaranteed to run everywhere and will run the same every time.
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R 3.4.4 is released
This update improves automatic timezone detection on some systems, and adds fixes for a some unusual corner cases in the statistics library. For a complete list of the changes, check the NEWS file for R 3.4.4 or follow the link above.
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Azure cloud data and AI services training roundup
AÂ roundup of on demand Azure cloud data and AI services training.
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âYou can't do data science in a GUIâ - Hadley Wickham
âYou canât do data science in a GUIâ, by Hadley Wickham, Chief Scientist at RStudio. Talk given at Chicago Chapter of the ACM, 7 March 2018.
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How 3 lines of code & Windows ML empower developers to run AI locally on Win 10 devices
Overview of what Windows ML can do for developers; and show how to easily use only three lines of code to develop UWP application that runs AI locally on Windows 10 devices.
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#ifdef WINML - Windows Machine Learning
Lucas Brodzinksi from the Windows Machine Learning team gives us an overview of what is Windows ML, why it is important, and how to get started.
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Introduction to Azure ML Services [Part 1/4]
In this episode, you will get an overview of Microsoftâs AI platform and we will do a walkthrough of different components of Azure ML Services.
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Setting Up Azure ML Services and Data wrangling using Azure ML services [Part 2/4]
In this episode, you will get started with Azure Machine Learning Services by setting up an Experimentation account, a Model Management account and AML Workbench. You will learn about the different features of Azure ML Workbench and its ability to speed up data wrangling.
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The Simplest Machine Learning
In this episode of the AI Show, machine learning is gently introduced from the standpoint of the algorithm and model. It starts with the simplest machine learning, linear regression, and ends on a de
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Demystifying AI
This session is a primer to introduce the concepts of deep learning with a specific focus on computer vision. It covers concepts including CNNâs (Convolutional Neural Networks), deep learning and transfer learning. It was created as an introduction for people getting started with machine learning and specifically deep learning to explain some of the commonly used terms and introduce some of the popular approaches to solving computer vision challenges.
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When And When Not To Use Deep Learning
A dive into when and why one would use deep learning over classical machine learning. While many tasks can be performed cheaply and well with classical machine learning and packages like scikit-learn, every once in a while, a task is better suited for a neural network architecture implemented with deep learning methods â e.g. large amounts of data or insufficient accuracy with other methods. Watch to find out more and hear about some Python packages to make life easier.
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Building Blocks of AI Interpretability
The paper âBuilding Blocks of Interpretabilityâ is available here.
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A Photo Enhancer AI
The paper âDSLR-Quality Photos on Mobile Devices with Deep Convolutional Networksâ and its demo is available here.
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AI-Based Animoji Without The iPhone X
The paper âAvatar Digitization From a Single Image For Real-Time Renderingâ is available here.
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Microsoft/malmo: Project Malmo
Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment.
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Prove It!: How to Create a High-Performance Culture and Measurable Success by Stacey Barr
The best leaders already know how their organisation is performing, and that it has improved during their tenure - and they can prove it because they practise evidence-based leadership. This book offers a clear blueprint for building on your existing skills and performance management systems to build a truly high performance organisation.
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Johannesburg, South Africa, 2020
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