Frank's Links

Github Repo that Provides Links to How to Build a Lot of Different Software-related Things https://github.com/codecrafters-io/build-your-own-x#build-your-own-database Github Repo that Provides Links to How to Build a Lot of Different Software-related Things: examples include: database, docker, cryptocurrency, game, git, network stack.

Anthropic Prompt Engineering https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview Link to prompt engineering guide by Anthropic. Prompt engineering is something that is emphasized in their documentation, so I am going to try to learn more about it.

Multimarkdown https://fletcherpenney.net/multimarkdown/ I was reading something about pandoc and this flavor of markdown looked interesting.

ollama https://ollama.com/ ollama is a way to run large language models locally. I have it installed on my machine.

Formatting Magick Identify Command https://imagemagick.org/script/escape.php There are copious amounts of extra data associated with images (metadata), beyond the actual image pixels. This metadata can be useful, either for display, or for various calculations, or in modifying the behavior of later image processing operations. You can utilize percent escapes in a number of options, for example in -format or in montage -label, to print various properties and other settings associated with an image.

Installing ffmpeg on Linux Machines https://www.johnvansickle.com/ffmpeg/faq/ Wanted to keep this link on installing ffmpeg on Linux machines in case I need to use it in the future.

National Gallery of Art Open Data https://github.com/NationalGalleryOfArt/opendata This link is for the national gallery of art open data program. I am going over style transfer and wanted good examples of art to use to test out the style transfer model and found this repo which I might want to use for something.

A Beginner's Guide to the True Order of SQL Operations https://blog.jooq.org/a-beginners-guide-to-the-true-order-of-sql-operations/ Learning more about SQL. Investigating how the lexical order of operations does not match the logical order of operations.

Pronunication Guide https://www.oxfordlearnersdictionaries.com/us/about/pronunciation_american_english Quick reference to look at when you don't understand a dictionary pronunciation.

Recurrent World Models Facilitate Policy Execution https://worldmodels.github.io/ We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own dream environment generated by its world model.

GloVe: Global Vectors for Word Representations https://nlp.stanford.edu/projects/glove/ GloVe is an unsupervised learning algorithm for obtaining vector representations of words. Training is performed on aggregtaed word-word co-occurence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. You can get pre-trained word vectors here.

Project Gutenberg http://www.gutenberg.net/ A collection of free ebooks that can be downloaded as plain text files. This is a great resource for sourcing data that can be used to train text-based deep learning models.

Keras-GAN Repository https://github.com/eriklindernoren/Keras-GAN Maintained by Erik Linder-Noren, this is an excellent resource for many Keras examples of important Generative Adverserial Networks (GANs) from the literature.

LSTM Chistopher Olah Blog Post https://colah.github.io/posts/2015-08-Understanding-LSTMs/ A blog post on understanding LSTM's by Christopher Olah that I was recommended to read. It is a complicated subject, so I wanted an additional resource on it.

Karpathy Article on Backpropagation https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b Explaining back propagation in English and some reasons there might be problems in Neural Networks. Gives a justification for computing backpropagation by hand at least once.

Feature Engineering Techniques for Machine Learning in Python https://www.youtube.com/watch Feature Engineering Techniques for Machine Learning in Python - one of the most important things when it comes to machine learning models.

NFL Stats https://nflsavant.com/about.php NFL statistics, including play-by-play.

Scikit-Learn Metrics for Evaluating Machine Learning Models https://scikit-learn.org/stable/modules/model_evaluation.html Links to a page that goes over all of the metrics that you might use to evaluate machine learning models.

Python Tutorial https://docs.python.org/3/tutorial/index.html Use if you ever want to review Python.

Papers with Code https://paperswithcode.com/ More and more ML papers are released along with their implementation, and sometimes even with pretrained models. Check out https://paperswithcode.com/ to easily find them.

Using Anaconda in VSCode https://docs.anaconda.com/working-with-conda/ide-tutorials/vscode/ Article goes into using anaconda in vscode. Might be helpful to look at sometime.

NIST Digital Library of Mathematical Functions https://dlmf.nist.gov/ The National Institute of Standards and Technology's Digital Library of Mathematical Functions is a great reference on math functions and math in general.

Github Issue Talking About Safari Making Content Invisible On Scroll https://github.com/janpaepke/ScrollMagic/issues/467 This is a Github issue that discusses the visibility of content being hidden on scroll in safari and ways to fix the issue.

Review Linear Algebra YouTube Course https://www.youtube.com/watch I want to review Linear Algebra and this looks ike a good YouTube course to review the information.

Graphics Magic Command Line Utility Documentation http://www.graphicsmagick.org/GraphicsMagick.html Command line utility for graphics magick "gm" documentation.

LaTeX Mathematical Symbols https://www.cmor-faculty.rice.edu/~heinken/latex/symbols.pdf I am pretty sure that I already have a link for Latex symbols, but here is another one.

iOS Hover Problem https://humanwhocodes.com/blog/2012/07/05/ios-has-a-hover-problem/ On mobile devices, safari has an issue where the click event does not fire on an element due to :hover styling. This article explains the issue with :hover in safari on mobile devices.

Google Site Reliability Engineering Document https://sre.google/sre-book/table-of-contents/ Site Reliability Engineering is focused on operating services atop distributed computing systems. "This book is an attempt to expan how we do things" - to keep Google services running. Might want to read.

Look Into Creating Lambda Layers for ImageMagick / ffmpeg / others https://serverless.pub/lambda-utility-layers/ This article / blog post written by Gojko Adzic goes into creating Lambda Layers that can be used to manipulate images / audio and video files / other using custom commands.

Magick++ Tutorial https://imagemagick.org/Magick++/tutorial/Magick++_tutorial.pdf A tutorial of the Magick++ graphics library - a graphics library that is a set of C++ wrapper classes the provide access to the ImageMagick package functionality from within a C++ application.

How to Change the Value of a File Input https://pqina.nl/blog/set-value-to-file-input/ How to change the value of an <input type="file"/> input from pqina.com.

Learning Docker https://github.com/dthtien/ebook-2/blob/master/Docker/Learning%20Docker.pdf Gitub repository with various resources for learning Docker. Docker is a set of platform as a service (PaaS) products that use OS-level virtualization to deliver software in packages called containers.

Ilya Sutskever Recommended Machine Learning Papers https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE Ilya Sutskever gave John Carmack this reading list of approx 30 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today.’ w.r.t. Machine Learning / Artificial Intelligence

Standard Go Project Layout https://github.com/golang-standards/project-layout This is a basic layout for Go application projects. Note that it's basic in terms of content because it's focusing only on the general layout and not what you have inside.

Matplotlib to D3 Python Library https://mpld3.github.io/ Library that you may want to look into for displaying Matplotlib graphs in the browser using d3.js

uNetworking JS Framework Github Rep https://github.com/uNetworking/uWebSockets.js Link is to the uWebworking JS github repository. This framework was written in C++ and offers much higher speeds than ExpressJS.

Google Style Guide for Python Docstrings https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings This webpage talks aboy Google's preferences for writing Python code. The section that is liked in the url links to the section of the webpage that talks about how Google formats their python docstrings.

Learning Numpy, Pandas, and Matplotlib Ebook https://python-course.eu/books/bernd_klein_python_data_analysis_a4.pdf Ebook to learn numpy, pandas, and Matplotlib that I am going to use to review the basics of python. To explore the language itself, I am going to be using "Learning Python" by Mark Lutz

Anthology of Modern ML Papers https://github.com/dmarx/anthology-of-modern-ml ASnothology of Machie Learning papers taht I may want to go through later after learning more of the basics

Scikit Learn Choosing Estimator https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html Links to a helpful diagram that should help you choose the right estimator top build machine learning models with

Markdown Quick Reference https://www.markdownguide.org/cheat-sheet/ Markdown quick reference

LaTeX Quick Reference https://icl.utk.edu/~mgates3/docs/latex.pdf Quick Reference for LaTeX

KaTeX Reference https://katex.org/docs/supported.html Reference for KaTeX Supported functions

Datasets Subreddit https://www.reddit.com/r/datasets Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

Wikipedia's List of Machine Learning Datasets https://homl.info/9 Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

Meta Portals Data Monitor http://opendatamonitor.eu/ Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

Meta Portals http://dataportals.org/ Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

Amazon's AWS Datasets https://registry.opendata.aws/ Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

Kaggle Datasets https://www.kaggle.com/datasets Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

UC Irvine Machine Learning Repository http://archive.ics.uci.edu/ml/ Link from Hands On Machine Learning With Scikit-Learn and Tensor Flow from section which shows resources where you can get data

Github Repo for Hands-On Machine Learning With Scikit / TensorFlow Textbook https://github.com/ageron/handson-ml2 Github repository for Hands-On Machine Learning with Scikit Learn and TensorFlw. This github repository contains some code that is not included in the textbook.

Good Youtube Channel for Learning Canvas https://www.youtube.com/@Frankslaboratory Found this really good youtube channel that can help you get better at HTML Canvas

Learning D3.js https://www.youtube.com/playlist Learning D3.js with this youtube tutorial + the docs (I don't find the docs to be that understandable).

Learning Python, Data Science, and ML https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.01-Help-And-Documentation.ipynb Github Repository containing jupiter notebooks that contain information on python, numpy, pandas, scikitlearn, and matplotlib for learning data science / machine learning

Learning Pandas https://www.youtube.com/watch I needed to learn the Pandas python library because the language seems to have changed a lot since I last did anything with it and the videos that I am watching on machine leartning application use an outdated version of pandas.

AWS SignIn https://signin.aws.amazon.com/signin Adding a Link to sign into the AWS console

Learning Applied AI https://www.youtube.com/watch Youtube channel that I am using to learn more about AI to work on some projects related to it. Mainly started watching these videos to learn about how to show vector embeddings related to poll results

Personal Website https://frankmbrown.net/ Link to my personal website

Visulaizing MNIST Database https://colah.github.io/posts/2014-10-Visualizing-MNIST/ Blog post on visualizing MNIST database. It provides a description of fimensional reductionality for the purpose of data visualization.

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