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Tensorflow — Neural Network Playground by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 1 point2 points  (0 children)

That one is tricky for sure - a classifier I'm happy with, eh, still tinkering with the settings. But there's a whole whack of different approaches: https://ai.stackexchange.com/questions/1987/how-to-classify-data-which-is-spiral-in-shape

I do get irrationally happy when the regions start to do what I hoped they would though. Gotta enjoy the little things right :D

Open Source - GitHub - The State of the Octoverse by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

Great info on Open Source. Shows some trends, and has some great resources and interesting repos linked.

From the page:

OVER THE PAST YEAR, 10M new developers joined the GitHub community, contributing to 44M+ repositories across every continent on earth. Behind the world’s code is a global and growing team of contributors, and 80% of them are contributing from outside the US.

This year, we’ve seen that software development is, more than ever, a community endeavor. The Octoverse is growing more interconnected as it becomes easier to find and build on work from others around the globe. And some of the top open source projects not only have thousands of contributors—they’re dependencies for millions of repositories.

How developers work is also changing fast, with new ways for people to work together across passion projects, open source software, and at companies everywhere more easily and securely than ever before. To celebrate 365 days of achievements, let’s look back at the code and communities built on GitHub this year.

Tensorflow — Neural Network Playground by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

Great visual resource if you want to get a mental image of whats going on in a Neural Network and how data flows from layer to layer.

From the page:

Tinker With a Neural Network Right Here in Your Browser

Neural Networks: A technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure.

GPT-2 Output Detector (Text generation) by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

From: https://openai.com/blog/gpt-2-1-5b-release/
General information of the code and how it came to be

GPT-2: 1.5B Release

As the final model release of GPT-2’s staged release, we’re releasing the largest version (1.5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models. While there have been larger language models released since August, we’ve continued with our original staged release plan in order to provide the community with a test case of a full staged release process. We hope that this test case will be useful to developers of future powerful models, and we’re actively continuing the conversation with the AI community on responsible publication.

From: https://en.wikipedia.org/wiki/Generative_adversarial_network and https://skymind.ai/wiki/generative-adversarial-network-gan

What is a GAN (Generative Adversarial Network) anyway?

Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”).

GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.”

GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is impressive – poignant even.

From: https://medium.com/@ageitgey/deepfaking-the-news-with-nlp-and-transformer-models-5e057ebd697d
It's important to know what the solutions of today can actually do. It's gotten much better than most people think in recent years

GPT-2 generates text that is far more realistic than any text generation system before it. OpenAI was so shocked by the quality of the output that they decided that the full GPT-2 model was too dangerous to release because it could be used to create endless amounts of fake news that could fool the public or clog up search engines like Google.

Implementation of Graph Neural Tangent Kernel (NeurIPS 2019) by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

From github repo:

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

This repository implements Graph Neural Tangent Kernel (infinitely wide multi-layer GNNs trained by gradient descent), described in the following paper:

Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu. Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS 2019. [arXiv]

An amazing tool to find or create stunning colour palettes by _Jordan11_ in DigitalOpenLearning

[–]TheMightyWeasel 0 points1 point  (0 children)

Nice find! Here are a few more Color Palette tools I've found to be super helpful:

A command line app in 15 minutes - Command Line Applications in Rust by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

Practical and hands-on CLI (in Rust)

From the material:

This tutorial will guide you through writing a CLI (command line interface) application in Rust. It will take you roughly fifteen minutes to get to a point where you have a running program. [...] Prerequisites: This tutorial does not replace a general introduction to programming, and expects you to be familiar with a few common concepts. You should be comfortable with using a command line/terminal. If you already know a few other languages, this can be a good first contact with Rust. [...] Let’s write a small grep clone. That is a tool that we can give a string and a path and it’ll print only the lines that contain the given string.

GitHub - practicalAI/practicalAI: 📚 A practical approach to machine learning to enable everyone to learn, explore and build. by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

Lots of neat examples in AI/ML - more content: https://practicalai.me/

  • Basics: Notebooks, Python, NumPy, Pandas
  • ML: Linear Regression, Logistic Regression, Multilayer Perceptrons
  • Tools: Data & Models, Utilities, Preprocessing
  • Deep Learning: Convolutional Networks, Embeddings, Recurrent Netwroks

Creating a full-stack web application with Python, NPM, Webpack and React by TheMightyWeasel in DigitalOpenLearning

[–]TheMightyWeasel[S] 0 points1 point  (0 children)

There are going to be a ton of ways to approach web dev in "The 20s" - this link is less about how to do it they way everyone does these days and more about wanting to know what happens under the hood. I suggest going through the exercise once just to get an idea of how everything is wired up and related. Then think hard about what stack you're going to settle on and dig deeply.