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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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Victor Zhou victorzhou.com

Random Forests for complete beginners

Victor Zhou has been killin’ it lately with these explainers: In my opinion, most Machine Learning tutorials aren’t beginner-friendly enough. Last month, I wrote an introduction to Neural Networks for complete beginners. This post will adopt the same strategy, meaning it again assumes ZERO prior knowledge of machine learning. We’ll learn what Random Forests are and how they work from the ground up.

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Netflix Technology Blog Icon Netflix Technology Blog

Python at Netflix

From the Netflix Technology Blog on how they’re using Python. As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.

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Hamel Husain towardsdatascience.com

How to automate tasks on GitHub with machine learning for fun and profit

This is an explainer on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets. Hamel Husain writes: In order to show you how to create your own apps, we will walk you through the process of creating a GitHub app that can automatically label issues. Note that all of the code for this app, including the model training steps are located in this GitHub repository. See also: Issue Label Bot

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NVIDIA Developer Blog Icon NVIDIA Developer Blog

NVIDIA Jetson Nano - A $99 computer for embedded AI

Google, Intel, and others have recently been targeting AI at the edge with things like Coral and the Neural Compute Stick, but NVIDIA is taking things a step farther. They just announced the Jetson Nano, which is a $99 computer with 472 GFLOPS of compute performance, an integrated NVIDIA GPU, and a Raspberry Pi form factor. According to NVIDIA: The compute performance, compact footprint, and flexibility of Jetson Nano brings endless possibilities to developers for creating AI-powered devices and embedded systems. And it’s not only for inference (which is the main target of things like Intel’s NCS). The Jetson Nano can also handle AI model training: since Jetson Nano can run the full training frameworks like TensorFlow, PyTorch, and Caffe, it’s also able to re-train with transfer learning for those who may not have access to another dedicated training machine and are willing to wait longer for results. Check it out! You can pre-order now.

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The Allen Institute for AI Icon The Allen Institute for AI

China to overtake US in AI research

China has committed to becoming the world leader in AI by 2030, with goals to build a domestic artificial intelligence industry worth nearly $150 billion (according to this CNN article). Prompted by these efforts, the Semantic Scholar team at the Allen AI Institute analyzed over two million academic AI papers published through the end of 2018. This analysis revealed the following: Our analysis shows that China has already surpassed the US in published AI papers. If current trends continue, China is poised to overtake the US in the most-cited 50% of papers this year, in the most-cited 10% of papers next year, and in the 1% of most-cited papers by 2025. Citation counts are a lagging indicator of impact, so our results may understate the rising impact of AI research originating in China. They also emphasize that US actions are making it difficult to recruit and retain foreign students and scholars, and these difficulties are likely to exacerbate the trend towards Chinese supremacy in AI research.

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OpenAI Icon OpenAI

OpenAI creates a "capped-profit" to help build artificial general intelligence

OpenAI, one of the largest and most influential AI research entities, was originally a non-profit. However, they just announced that they are creating a “capped-profit” entity, OpenAI LP. This capped-profit entity will supposedly help them accomplish their mission of building artificial general intelligence (AGI): We want to increase our ability to raise capital while still serving our mission, and no pre-existing legal structure we know of strikes the right balance. Our solution is to create OpenAI LP as a hybrid of a for-profit and nonprofit—which we are calling a “capped-profit” company. The fundamental idea of OpenAI LP is that investors and employees can get a capped return if we succeed at our mission, which allows us to raise investment capital and attract employees with startup-like equity. But any returns beyond that amount—and if we are successful, we expect to generate orders of magnitude more value than we’d owe to people who invest in or work at OpenAI LP—are owned by the original OpenAI Nonprofit entity. To some this makes total sense. Others have criticized the move, because they say that it misrepresents money as the only barrier to AGI or implies that OpenAI will develop it in a vacuum. What do you think? Learn more about OpenAI’s mission from one of it’s founders in this episode of Practical AI.

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Python github.com

GIPHY's celebrity-detecting deep learning model 🕵️‍♀️

GIPHY is proud to release our custom machine learning model that is able to discern over 2,300 celebrity faces with 98% accuracy. The model was trained to identify the most popular celebs on GIPHY, and can identify and make predictions for multiple faces across a sequence of images, like GIFs and videos. Give it a try on the demo page or download the model yourself and follow along with the examples.

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Victor Zhou victorzhou.com

Machine learning for beginners

Victor Zhou writing on machine learning for beginners with this introduction to neural networks. …neural networks aren’t that complicated! The term “neural network” gets used as a buzzword a lot, but in reality they’re often much simpler than people imagine. This post is intended for complete beginners and assumes ZERO prior knowledge of machine learning. We’ll understand how neural networks work while implementing one from scratch in Python.

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AI (Artificial Intelligence) towardsdatascience.com

A response to OpenAI's new dangerous text generator

Those of you following AI related things on Twitter have probably been overwhelmed with commentary about OpenAI’s new GPT-2 language model, which is “Too Dangerous to Make Public” (according to Wired’s interpretation of OpenAI’s statements). Is this discussion frustrating or confusing for you? Well, Ryan Lowe from McGill University has published a nice response article. He discusses the model and results in general, but also gives some perspective on the ethical implication and where the AI community should go from here. According to Lowe: “The machine learning community really, really needs to start talking openly about our standards for ethical research release”

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Machine Learning fastcompany.com

Pentagram designed the prettiest computer chip you’ve ever seen

These IPUs (Intelligence Processing Units — a term new to me) with visual design by Pentagram for Graphcore are really pretty. Also, I think the tech may be cool but it’s a bit over my head so maybe you can tell me? Here is their brief spiel: Our IPU systems are designed to lower the cost of accelerating AI applications in cloud and enterprise datacenters to increase the performance of both training and inference by up to 100x compared to the fastest systems today.

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Facebook Engineering Blog Icon Facebook Engineering Blog

Facebook has a tool that learns to fix bugs automatically?!

This week on the Facebook code blog they shared details about a new tool called Getafix that automatically finds fixes for bugs and offers them to engineers to approve. 😎 Modern production codebases are extremely complex and are updated constantly. To create a system that can automatically find fixes for bugs — without help from engineers — we built Getafix to learn from engineers’ previous changes to the codebase. It finds hidden patterns and uses them to identify the most likely remediations for new bugs. Getafix has been deployed to production at Facebook, where it now contributes to the stability of apps that billions of people use. The goal of Getafix is to let computers take care of the routine work, albeit under the watchful eye of a human, who must decide when a bug requires a complex, nonroutine remediation. Whether or not this tool will be open sourced or shared at large remains to be seen. How cool would it be to have something like this deployed to your codebase to find and suggest fixes to your bugs?

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Go blog.sourced.tech

Detecting licenses in code with Go and ML

Why not just query GitHub’s API to get the licenses? we were not satisfied with its detection quality: many projects which actually contain the license file in a non-standard format are missed, and some are misclassified. What they came up with is go-license-detector, which detects 99% of licenses in a test dataset (compared to GitHub’s 75%) in a fraction of the time. And the winner is… MIT.

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Smashing Magazine Icon Smashing Magazine

Making a mobile app with facial recognition features

This article isn’t a how-to, per se. It’s more like a research report written after attempting to build such an app for the first time. There’s nothing wrong with that, though, and this write-up is super useful if you’re about to tackle a similar problem space. Open source libraries are tried, facial recognition services are evaluated, and their takeaways are solid, if not a bit disappointing. As you can see, the really simple idea of using facial recognition functionality was not that simple to implement. The entire piece is worth a read.

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Machine Learning haacked.com

Analyzing GitHub issue comment sentiment with Azure

If you’ve been looking to dabble in some AI and serverless, Phil Haack shared his process to create a SentimentBot for GitHub issues with Azure Functions. Perhaps the combination of machine learning and human judgement could make the problem more tractable. I decided to play around with Azure Functions because they have specific support for GitHub Webhooks. GitHub Webhooks and Azure Functions go together like Bitters and Bourbon. If you want to skip the code and just test it out, head to this issue.

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Machine Learning varianceexplained.org

What's the difference between data science, machine learning, and AI?

We’ve needed this post for a very long time. Thank you David Robinson. When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” But that overlap, tho. The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable. Most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words. Here’s the break down…

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