applied-ml
React
applied-ml | React | |
---|---|---|
13 | 1,696 | |
25,984 | 222,111 | |
- | 0.6% | |
3.0 | 9.9 | |
5 days ago | 3 days ago | |
JavaScript | ||
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
applied-ml
-
[D] Favorite ML Youtube Channels/Blogs/Newsletters
Also, have any of you stumbled across any cool GitHub repos like this one: https://github.com/eugeneyan/applied-ml ?
- Curated Papers on Machine Learning in Production
-
Top Github repo trends in 2021
The second repo I LOVE is Eugene Yan’s Applied ML repository. This is a brilliant idea to create and actually something I was planning on sort of casually doing in my non-existent free time… Anyhow, it is a curated list of technical posts from top engineering teams (Netflix, Amazon, Pinterest, Linkedin, etc.) detailing how they built out different types of AI/ML systems (e.g. forecasting, recommenders, search and ranking, etc.). Ofc, it focuses on AI/ML, but something similar could be made for the traditional or BI-oriented analytics stack, as well as the streaming world, super high value for practitioners! Btw-one of my favorite things at BCG used to be looking at our IT architecture team’s reference architecture diagrams… the best way to understand technologies is to look at how a ton of stuff is architected… and its fun!
- Curated papers, articles, & blogs on data science and ML in production
-
Messed up my career by pivoting to DS. Wondering if it's too late to switch to MLE
Applied ML: A collection of papers, articles, and blogs on ML in production by different companies (Netflix, Uber, Facebook, LinkedIn, etc)
-
[D] A dilemma of an ML guy in industry
Eugene Yan's applied-ml has tons of case studies.
- Papers & tech blogs by companies sharing their work on data science & machine learning in production.
-
My information dump for people trying to break into data science/interview notes
https://github.com/eugeneyan/applied-ml You may find some of his links interesting. I would avoid anything that refers to scaling up a platform as these are more backend engr focus. The more relevant posts to you are probably on the scale of blog posts that are product oriented like the ones I listed in section 4 (e.g. we wanted to solve X for our users and this is how we scoped and defined it). The technical aspects should come backseat to the business aspects. There's def a lot of companies/blog posts that he missed, but the internet is huge.
-
[D] Can anyone point me to resources/case studies of companies/business creating infrastructure for their data needs?
Check the resources mentioned in applied-ml. It includes blog posts/papers from many companies describing how they built some ML product X.
-
What content would be useful to intermediate Data Scientist
Check out this repo. They collect hundreds of case studies, broken down by dozens of methodologies from large real-world companies such as AirBnB, Nvidia, Uber, Netflix etc.
React
-
Inflight Magazine no. 9
We are continuing to add new project templates for various types of projects, and we've recently created one for the infamous combination of React with Vite tooling.
-
"Kawaii" tech logos by Sawaratsuki
Go to https://react.dev/?uwu=true for a surprise.
-
Building an Email Assistant Application with Burr
You can use any frontend framework you want — react-based tooling, however, has a natural advantage as it models everything as a function of state, which can map 1:1 with the concept in Burr. In the demo app we use react, react-query, and tailwind, but we’ll be skipping over this largely (it is not central to the purpose of the post).
-
React 18.3.0 Is Out
Oddly, no info on changelog: https://github.com/facebook/react/blob/main/CHANGELOG.md
-
Preact vs React: A Comparative Guide
In this post, we get to know more about Preact, one of this year's trending libraries. And we'll compare it to React to see which one suits better for our projects.
-
Meet Cheryl Murphy: Full-Stack Developer, lifelong learner, and volunteer Project Team Lead at Web Dev Path
Cheryl Murphy is not only a dedicated full-stack web developer skilled in technologies like React, Next.js, and NestJs but also a community-driven professional who recently took on the role of volunteer project team lead at Web Dev Path. With a dual Bachelor's degree in Computing and Chemical Engineering from Monash University, Cheryl’s journey in tech is marked by a passion for building accessible solutions and a commitment to fostering community within tech.
-
How to Build an AI FAQ System with Strapi, LangChain & OpenAI
Basic knowledge of ReactJs
-
Everyone Has JavaScript, Right?
Google Translate and many other libraries break React based sites if they are using refs.
I don't think that point it falls under "written on naive assumptions"
https://github.com/facebook/react/issues/11538
the issue says closed but you can easily catch it in various sites and use cases.
-
Integrate Bootstrap with React
This article serves as your comprehensive guide to mastering the art of combining Bootstrap and React seamlessly. Dive in to uncover the tips, tricks, and best practices to elevate your UI design game effortlessly.
-
React Server Components Example with Next.js
This isn’t an accident; when Meta introduced React Server Components, Dan Abramov explicitly stated that they collaborated with the Next.js team to develop the RSC webpack plugin.
What are some alternatives?
awesome-mlops - A curated list of references for MLOps
qwik - Instant-loading web apps, without effort
awesome-ml-blogs - Curated list of technical blogs on machine learning · AI/ML/DL/CV/NLP/MLOps
Alpine.js - A rugged, minimal framework for composing JavaScript behavior in your markup.
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
Vue.js - This is the repo for Vue 2. For Vue 3, go to https://github.com/vuejs/core
Cookbook - The Data Engineering Cookbook
SvelteKit - web development, streamlined
ml-surveys - đź“‹ Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
lit-element - LEGACY REPO. This repository is for maintenance of the legacy LitElement library. The LitElement base class is now part of the Lit library, which is developed in the lit monorepo.
pipebase - data integration framework
Tailwind CSS - A utility-first CSS framework for rapid UI development.