stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning (by afshinea)
applied-ml
đź“š Papers & tech blogs by companies sharing their work on data science & machine learning in production. (by eugeneyan)
| stanford-cs-229-machine-learning | applied-ml | |
|---|---|---|
| 1 | 13 | |
| 16,526 | 25,984 | |
| - | - | |
| 0.0 | 3.0 | |
| almost 4 years ago | 6 days ago | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
stanford-cs-229-machine-learning
Posts with mentions or reviews of stanford-cs-229-machine-learning.
We have used some of these posts to build our list of alternatives
and similar projects.
applied-ml
Posts with mentions or reviews of applied-ml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-12.
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[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
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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
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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)
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[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.
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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.
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[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.
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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.
What are some alternatives?
When comparing stanford-cs-229-machine-learning and applied-ml you can also consider the following projects:
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.
awesome-mlops - A curated list of references for MLOps
modern-php-cheatsheet - Cheatsheet for some PHP knowledge you will frequently encounter in modern projects.
awesome-ml-blogs - Curated list of technical blogs on machine learning · AI/ML/DL/CV/NLP/MLOps
fsharp-cheatsheet - An updated cheat sheet for F# 🔷🦔💙💛🤍💚
mongodb-cheatsheet - Kick start with mongodb
Cookbook - The Data Engineering Cookbook
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
ml-surveys - đź“‹ Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
MoneroAddressesCS - An infographic about Monero Keys and Addresses, their relations and scopes
pipebase - data integration framework
stanford-cs-229-machine-learning vs machine-learning-roadmap
applied-ml vs awesome-mlops
stanford-cs-229-machine-learning vs modern-php-cheatsheet
applied-ml vs awesome-ml-blogs
stanford-cs-229-machine-learning vs fsharp-cheatsheet
applied-ml vs machine-learning-roadmap
stanford-cs-229-machine-learning vs mongodb-cheatsheet
applied-ml vs Cookbook
stanford-cs-229-machine-learning vs pyod
applied-ml vs ml-surveys
stanford-cs-229-machine-learning vs MoneroAddressesCS
applied-ml vs pipebase