#javascript#ecmascript_proposals#es2015#es2019#es6#es7#esnext#javascript#js#polyfill#ponyfill#promise#proposal#proposals#shim#symbol#weakmap
core-js is a modular JavaScript library that provides polyfills for modern ECMAScript features up to 2024, including promises, symbols, collections, iterators, typed arrays, and many web standards like URL and structuredClone. It lets you use new JavaScript features in older browsers by loading only the needed parts without polluting the global namespace. It integrates well with tools like Babel and swc for optimized polyfilling. This helps you write modern, compatible code that runs smoothly across different environments, improving development efficiency and user experience. You can customize polyfill usage and even build your own tailored version for your project.
https://github.com/zloirock/core-js
#ML
😎
FREE RESOURCES TO LEARN MACHINE LEARNING
Intro to ML by MIT Free Course
Machine Learning for Everyone FREE BOOK
ML Crash Course by Google
Advanced Machine Learning with Python Github
Practical Machine Learning Tools and Techniques Free Book
Python Machine Learning for beginners
-----
Main channel: @repo_science
Coupons: @freecoupons_reposcience
-----
#ML
🧠
Machine Learning Expert
El aprendizaje automático es un vasto campo con muchos conceptos clave que conocer. Nuestro curso intensivo cubre todos los componentes básicos que necesita para sumergirse en el aprendizaje automático del mundo real.
✍️Ryan Doan | Ex-Amazon ML Infrastructure Engineer
🌐En
📆2022
🔗Link
-----
Main channel:@repo_science
Coupons:@freecoupons_reposcience
-----
#ml
What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings
https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/
#ml
Meta's second version of segment anything.
https://github.com/facebookresearch/segment-anything-2
They have a nice demo:
https://sam2.metademolab.com/
#ml
I was searching for a tool to visualize computational graphs and ran into this preprint. The hierarchical visualization idea is quite nice.
https://arxiv.org/abs/2212.10774
#ml
Like a dictionary
Kunc, Vladim’ir, and Jivr’i Kl’ema. 2024. “Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.09092.
#ml
I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions.
Then I saw this paper today:
Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
#ml
Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032
Not surprising at all when you have direct access to a long context. But hey, look at this title.