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    <description>recent bookmarks from nico.ash</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://salt.agency/blog/nlp-and-stuff/"/>
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	<rdf:li rdf:resource="https://pjreddie.com/media/files/papers/YOLOv3.pdf"/>
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	<rdf:li rdf:resource="https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/"/>
	<rdf:li rdf:resource="https://openai.com/blog/generative-models/"/>
	<rdf:li rdf:resource="https://github.com/saiprashanths/dl-setup"/>
	<rdf:li rdf:resource="http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html"/>
	<rdf:li rdf:resource="http://derandomized.com/post/20009997725/bayes-net-example-with-python-and-khanacademy"/>
	<rdf:li rdf:resource="http://thunderboltlabs.com/posts/machine-learning-on-the-cheap-and-easy"/>
	<rdf:li rdf:resource="http://eferm.com/machine-learning-cheat-sheet"/>
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  </channel><item rdf:about="https://salt.agency/blog/nlp-and-stuff/">
    <title>Classifying 200,000 articles in 7 hours using NLP - SALT.agency®</title>
    <dc:date>2020-07-09T07:04:42+00:00</dc:date>
    <link>https://salt.agency/blog/nlp-and-stuff/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[Every now and then, consultancy firms face challenges that are abnormal and create new solutions. We faced that last year, to classify over a quarter of a million articles with a very limited budget. Our target was that if we can classify over 80% of them automatically with a 90% accuracy, then we can do the rest manually within budget; and that’s what we exactly did, through the use of Artificial Intelligence.]]></description>
<dc:subject>machine-learning nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:0db618e0c023/</dc:identifier>
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<item rdf:about="https://huyenchip.com/2020/06/22/mlops.html">
    <title>What I learned from looking at 200 machine learning tools</title>
    <dc:date>2020-07-06T10:28:35+00:00</dc:date>
    <link>https://huyenchip.com/2020/06/22/mlops.html</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[VI. Conclusion

There has been a lot of talk on whether the AI bubble will burst. A large portion of AI investment is in self-driving cars, and as fully autonomous vehicles are still far from being a commodity, some hypothesize that investors will lose hope in AI altogether. Google has freezed hiring for ML researchers. Uber laid off the research half of their AI team. There’s rumor that due to a large number of people taking ML courses, there will be far more people with ML skills than ML jobs.

Is it still a good time to get into ML? I believe that the AI hype is real and at some point, it has to calm down. That point might have already happened. However, I don’t believe that ML will disappear. There might be fewer companies that can afford to do ML research, but there will be no shortage of companies that need tooling to bring ML into their production.]]></description>
<dc:subject>ai machine-learning machinelearning ml</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:484edcc3cc66/</dc:identifier>
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<item rdf:about="https://news.ycombinator.com/item?id=23490115">
    <title>Ask HN: My wife might lose the ability to speak in 3 weeks – how to prepare? | Hacker News</title>
    <dc:date>2020-06-16T21:52:33+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=23490115</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>culture language software speech machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:9826a99bc5e5/</dc:identifier>
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<item rdf:about="https://github.com/microsoft/hummingbird">
    <title>microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.</title>
    <dc:date>2020-06-10T14:08:58+00:00</dc:date>
    <link>https://github.com/microsoft/hummingbird</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models. Thanks to Hummingbird, users can benefit from: (1) all the current and future optimizations implemented in neural network frameworks; (2) native hardware acceleration; (3) having a unique platform to support for both traditional and neural network models; and have all of this (4) without having to re-engineer their models.]]></description>
<dc:subject>compiler ai machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:c394c072f68e/</dc:identifier>
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<item rdf:about="https://simonwillison.net/2020/May/21/dogsheep-photos/">
    <title>Using SQL to find my best photo of a pelican according to Apple Photos</title>
    <dc:date>2020-05-25T09:51:08+00:00</dc:date>
    <link>https://simonwillison.net/2020/May/21/dogsheep-photos/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[How this works

Apple Photos keeps photo metadata in a SQLite database. It runs machine learning models to identify the contents of every photo, and separate machine learning models to calculate quality scores for those photographs. All of this data lives in SQLite files on my laptop. The trick is knowing where to look.

I’m not running queries directly against the Apple Photos SQLite file—it’s a little hard to work with, and the label metadata is stored in a separate database file. Instead, this query runs against a combined database created by my new dogsheep-photos tool.]]></description>
<dc:subject>apple development ios photography sql machinelearning machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:ec3b18175641/</dc:identifier>
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<item rdf:about="https://amitness.com/2020/03/fixmatch-semi-supervised/">
    <title>The Illustrated FixMatch for Semi-Supervised Learning</title>
    <dc:date>2020-04-14T23:29:40+00:00</dc:date>
    <link>https://amitness.com/2020/03/fixmatch-semi-supervised/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[FixMatch is a recent semi-supervised approach by Sohn et al. from Google Brain that improved the state of the art in semi-supervised learning(SSL). It is a simpler combination of previous methods such as UDA and ReMixMatch. In this post, we will understand the concept of FixMatch and also see it got 78% accuracy on CIFAR-10 with just 10 images.]]></description>
<dc:subject>machine-learning ml</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:cbfa4685471b/</dc:identifier>
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<item rdf:about="https://github.com/caoscott/SReC">
    <title>GitHub - caoscott/SReC: PyTorch Implementation of &quot;Lossless Image Compression through Super-Resolution&quot;</title>
    <dc:date>2020-04-07T21:43:29+00:00</dc:date>
    <link>https://github.com/caoscott/SReC</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[This is the official implementation of SReC in PyTorch. SReC frames lossless compression as a super-resolution problem and applies neural networks to compress images. SReC can achieve state-of-the-art compression rates on large datasets with practical runtimes. Training, compression, and decompression are fully supported and open-sourced.]]></description>
<dc:subject>compression imageprocessing machine-learning machinelearning image optimisation python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:203d83862f84/</dc:identifier>
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<item rdf:about="https://dotscience.com/product/">
    <title>Collaboration Tooling for End-to-End ML Data &amp; Model Management</title>
    <dc:date>2019-09-30T21:32:59+00:00</dc:date>
    <link>https://dotscience.com/product/</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>machine-learning machinelearning</dc:subject>
<dc:identifier>https://pinboard.in/u:nico.ash/b:b3a69ea99283/</dc:identifier>
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<item rdf:about="https://www.wandb.com/articles/monitor-your-pytorch-models-with-five-extra-lines-of-code">
    <title>Weights &amp; Biases - Monitor Your PyTorch Models With Five Extra Lines of Code</title>
    <dc:date>2019-09-30T21:32:45+00:00</dc:date>
    <link>https://www.wandb.com/articles/monitor-your-pytorch-models-with-five-extra-lines-of-code</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>machinelearning machine-learning py-torch</dc:subject>
<dc:identifier>https://pinboard.in/u:nico.ash/b:7a84a6f3db1f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:nico.ash/t:machinelearning"/>
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</item>
<item rdf:about="https://enlight.nyc/projects/neural-network/">
    <title>Build a Neural Network in Python | Enlight</title>
    <dc:date>2019-05-19T21:20:46+00:00</dc:date>
    <link>https://enlight.nyc/projects/neural-network/</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai learning machine-learning programming python</dc:subject>
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<item rdf:about="https://pjreddie.com/media/files/papers/YOLOv3.pdf">
    <title>YOLOv3 Machine learning object detection and labeling</title>
    <dc:date>2018-12-17T00:02:40+00:00</dc:date>
    <link>https://pjreddie.com/media/files/papers/YOLOv3.pdf</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>deeplearning machine-learning image classification</dc:subject>
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<item rdf:about="https://eng.uber.com/differentiable-plasticity/">
    <title>Differentiable Plasticity: A New Method for Learning to Learn | Uber Engineering Blog</title>
    <dc:date>2018-04-25T20:45:45+00:00</dc:date>
    <link>https://eng.uber.com/differentiable-plasticity/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[ Uber AI Labs has developed a new method called differentiable plasticity that lets us train the behavior of plastic connections through gradient descent so that they can help previously-trained networks adapt to future conditions. While evolving such plastic neural networks is a longstanding area of research in evolutionary computation, to our knowledge the work introduced here is the first to show it is possible to optimize plasticity itself through gradient descent. Because gradient-based methods underlie many of the recent spectacular breakthroughs in artificial intelligence (including image recognition, machine translation, Atari video games, and Go playing), making plastic networks amenable to gradient descent training may dramatically expand the power of both approaches. ]]></description>
<dc:subject>ai learning machine-learning ml</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:f4537e76f87f/</dc:identifier>
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<item rdf:about="https://jacquesmattheij.com/sorting-two-metric-tons-of-lego">
    <title>Sorting 2 Metric Tons of Lego · Jacques Mattheij</title>
    <dc:date>2017-04-30T23:04:13+00:00</dc:date>
    <link>https://jacquesmattheij.com/sorting-two-metric-tons-of-lego</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>fun hacking hardware lego deeplearning machine-learning computervision opencv</dc:subject>
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<item rdf:about="https://news.ycombinator.com/item?id=14226889">
    <title>Show HN: Sorting Two Metric Tons of Lego | Hacker News</title>
    <dc:date>2017-04-30T23:01:20+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=14226889</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[A thread about a self-built machine that sort bulk lego parts into bins using machine learning.]]></description>
<dc:subject>machine-learning lego opencv</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:c4aa4255b7b6/</dc:identifier>
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<item rdf:about="http://yerevann.com/a-guide-to-deep-learning/">
    <title>A Guide to Deep Learning by YerevaNN</title>
    <dc:date>2016-12-29T02:58:39+00:00</dc:date>
    <link>http://yerevann.com/a-guide-to-deep-learning/</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai machine-learning machinelearning tutorial ml</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:0b80b2eaeca6/</dc:identifier>
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<item rdf:about="https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/">
    <title>The major advancements in Deep Learning in 2016 - Tryolabs Blog</title>
    <dc:date>2016-12-07T21:02:42+00:00</dc:date>
    <link>https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai machine-learning neuralnetwork</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:7269bab96aa6/</dc:identifier>
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<item rdf:about="https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/">
    <title>A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – CS Undergrad at UCLA ('19)</title>
    <dc:date>2016-07-25T21:08:00+00:00</dc:date>
    <link>https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai machine-learning machinelearning math ml</dc:subject>
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<dc:identifier>https://pinboard.in/u:nico.ash/b:5ff929091178/</dc:identifier>
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