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    <title>Run Stable Diffusion on Your M1 Mac’s GPU | Hacker News</title>
    <dc:date>2022-09-01T20:50:59+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=32678664</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai tips ml mac apple</dc:subject>
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<item rdf:about="https://replicate.com/blog/run-stable-diffusion-on-m1-mac">
    <title>Run Stable Diffusion on your M1 Mac’s GPU - Replicate – Replicate</title>
    <dc:date>2022-09-01T20:49:37+00:00</dc:date>
    <link>https://replicate.com/blog/run-stable-diffusion-on-m1-mac</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai howto ml apple mac</dc:subject>
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<item rdf:about="https://news.ycombinator.com/item?id=25253598">
    <title>Apple Create ML: Creating an Image Classifier Model | Hacker News</title>
    <dc:date>2020-12-01T00:08:41+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=25253598</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[A thread full of machine learning information about using CreateML and machine learning]]></description>
<dc:subject>ai Apple CreateML MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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    <title>Sudowrite</title>
    <dc:date>2020-08-13T22:27:51+00:00</dc:date>
    <link>https://www.sudowrite.com/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[Bust writer’s block and be more creative with our magical writing AI.]]></description>
<dc:subject>ai writing nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:55707c2f66c8/</dc:identifier>
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<item rdf:about="https://github.com/JaidedAI/EasyOCR">
    <title>JaidedAI/EasyOCR: Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai</title>
    <dc:date>2020-07-08T15:13:57+00:00</dc:date>
    <link>https://github.com/JaidedAI/EasyOCR</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[Usage

import easyocr
reader = easyocr.Reader(['th','en'])
reader.readtext('test.jpg')

Note: Instead of filepath 'test.jpg', you can also pass OpenCV image object or image file as bytes.

Model weight for chosen language will be automatically downloaded or you can download it manually from the following links and put it in '~/.EasyOCR/model' folder]]></description>
<dc:subject>ai github ocr opensource python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:659a556c76c6/</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://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://github.com/minimaxir/aitextgen">
    <title>minimaxir/aitextgen: A robust Python tool for text-based AI training and generation using GPT-2.</title>
    <dc:date>2020-05-20T17:30:13+00:00</dc:date>
    <link>https://github.com/minimaxir/aitextgen</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 architecture.

aitextgen is a Python package that leverages PyTorch, Huggingface Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features. It is the successor to textgenrnn and gpt-2-simple, taking the best of both packages:

    Finetunes on a pretrained 124M GPT-2 model from OpenAI...or create your own GPT-2 model + tokenizer and train from scratch!
    Generates text faster than gpt-2-simple and with better memory efficiency! (even from the 1.5B GPT-2 model!)
    With Transformers, aitextgen preserves compatibility with the base package, allowing you to use the model for other NLP tasks, download custom GPT-2 models from the Huggingface model repository, and upload your own models! Also, it uses the included generate() function to allow a massive amount of control over the generated text.
    With pytorch-lightning, aitextgen trains models not just on CPUs and GPUs, but also multiple GPUs and (eventually) TPUs! It also includes a pretty training progress bar, with the ability to add optional loggers.
    The input dataset is its own object, allowing you to not only easily encode megabytes of data in seconds, cache, and compress it on a local computer before transporting to a remote server, but you are able to merge datasets without biasing the resulting dataset, or cross-train on multiple datasets to create blended output.

You can read more about aitextgen in the documentation!]]></description>
<dc:subject>ai python textgeneration deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://github.com/cyrildiagne/ar-cutpaste">
    <title>GitHub - cyrildiagne/ar-cutpaste: Cut and paste your surroundings using AR</title>
    <dc:date>2020-05-16T22:22:57+00:00</dc:date>
    <link>https://github.com/cyrildiagne/ar-cutpaste</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[AR Cut & Paste

An AR+ML prototype that allows cutting elements from your surroundings and pasting them in an image editing software.

Although only Photoshop is being handled currently, it may handle different outputs in the future.

Demo & more infos: Thread

⚠️ This is a research prototype and not a consumer / photoshop user tool.
Modules

This prototype runs as 3 independent modules:

    The mobile app
        Check out the /app folder for instructions on how to deploy the app to your mobile.

    The local server
        The interface between the mobile app and Photoshop.
        It finds the position pointed on screen by the camera using screenpoint
        Check out the /server folder for instructions on configuring the local server

    The object detection / background removal service
        For now, the salience detection and background removal are delegated to an external service
        It would be a lot simpler to use something like DeepLap directly within the mobile app. But that hasn't been implemented in this repo yet.

]]></description>
<dc:subject>adobe photoshop ai ml augmented-reality</dc:subject>
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</item>
<item rdf:about="https://mycroft.ai/about-mycroft/">
    <title>About Mycroft | The Open Source Artificial Intelligence Voice Assistant</title>
    <dc:date>2020-03-30T23:06:26+00:00</dc:date>
    <link>https://mycroft.ai/about-mycroft/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[

Mycroft is the world’s first open source voice assistant.

Our software runs on many platforms—on desktop, our Mycroft Mark 1, or on a Raspberry Pi. This is open source software which can be freely remixed, extended, and improved. Mycroft may be used in anything from a science project to an enterprise software application.
]]></description>
<dc:subject>ai raspberrypi siri voiceassistant</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://news.ycombinator.com/item?id=20674745">
    <title>Ask HN: What Neural Networks/Deep Learning Books Should I Read? | Hacker News</title>
    <dc:date>2019-08-15T22:13:49+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=20674745</link>
    <dc:creator>nico.ash</dc:creator><dc:subject>ai book books list machinelearning deeplearning neuralnetwork</dc:subject>
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<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://dragan.rocks/">
    <title>Deep Learning in Clojure from Scratch to GPU</title>
    <dc:date>2019-05-06T18:57:39+00:00</dc:date>
    <link>https://dragan.rocks/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[An introduction to a series of tutorials about Deep Learning in Clojure funded by Clojurists Together. Start with an empty clj file and build a fast neural network that runs on the GPU, built with nothing else but plain Clojure and Neanderthal. The series is a companion to a free online book Neural Networks and Deep Learning.]]></description>
<dc:subject>blog clojure gpu opencl ai deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:nico.ash/b:ba9cd42241d7/</dc:identifier>
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Computer programs that learn to perform tasks also typically forget them very quickly. We show that the learning rule can be modified so that a program can remember old tasks when learning a new one. This is an important step towards more intelligent programs that are able to learn progressively and adaptively.]]></description>
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<item rdf:about="http://yerevann.com/a-guide-to-deep-learning/">
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<item rdf:about="https://github.com/kendricktan/suiron/">
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<item rdf:about="http://culurciello.github.io/tech/2016/06/04/nets.html">
    <title>Neural Network Architectures</title>
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I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning.]]></description>
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    <title>[1606.04474] Learning to learn by gradient descent by gradient descent</title>
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<item rdf:about="https://openai.com/blog/generative-models/">
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<item rdf:about="https://blog.rescale.com/train-your-tensorflow-models-on-rescale/">
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<item rdf:about="http://jgap.sourceforge.net/">
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<item rdf:about="http://visitmix.com/labs/ai2canvas/index.html">
    <title>MIX Online:Ai to Canvas Plug-In</title>
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<item rdf:about="http://metaoptimize.com/qa/questions/186/">
    <title>Good Freely Available Textbooks on Machine Learning - MetaOptimize Q+A</title>
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    <title>Ma bibliothèque</title>
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    <title>Build An Optimal Scientist, Then Retire | h+ Magazine</title>
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<item rdf:about="http://blog.wolfire.com/2010/01/An-Introduction-to-AI-in-Games-from-Phil-Carlisle">
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<item rdf:about="http://www.phy.mtu.edu/~lshamir/downloads/ImageClassifier/">
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<item rdf:about="http://gregegan.customer.netspace.net.au/MISC/ORACLE/Oracle.html">
    <title>Oracle</title>
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<item rdf:about="http://gregegan.customer.netspace.net.au/MISC/SINGLETON/Singleton.html">
    <title>Singleton</title>
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<item rdf:about="http://www.allartburns.org/2009/07/29/fear-of-sentient-robots/">
    <title>ALL ART BURNS » fear of sentient robots</title>
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<item rdf:about="http://www.kk.org/thetechnium/archives/2008/03/turingd.php">
    <title>Kevin Kelly -- The Technium</title>
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    <link>http://www.kk.org/thetechnium/archives/2008/03/turingd.php</link>
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<item rdf:about="http://norvig.com/">
    <title>Peter Norvig</title>
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    <link>http://norvig.com/</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[the homepage of peter Norvig, with many links to software design articles and software examples in python and lisp. He now works at google as a director of research.
]]></description>
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    <title>Seeing Around Corners</title>
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    <link>http://www.theatlantic.com/doc/200204/rauch</link>
    <dc:creator>nico.ash</dc:creator><description><![CDATA[using artificial socirty to get a peek at society and possible outcomes of policy
]]></description>
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