#javascript#atari_lynx#atari2600#atari7800#ds#emulation#emulator#emulatorjs#gameboy#n64#nes#nintendo#nintendo_game_boy#playstation#retroarch#retroarch_wasm#sega_cd#sega_mega_drive#sega_saturn#snes#virtualboy
EmulatorJS is a free, open-source web-based emulator that lets you play classic games from Nintendo, Sega, Atari, and other systems directly in your browser without downloading software. It offers save states to resume games where you left off, customizable controls for keyboards and gamepads, and screen recording to share gameplay. The emulator runs entirely in your browser using JavaScript, supports multiple languages, and can be easily embedded into websites. You benefit from instant access to retro gaming across devices, no installation required, and the ability to build your own self-hosted gaming collection.
https://github.com/EmulatorJS/EmulatorJS
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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.
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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/
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Meta's second version of segment anything.
https://github.com/facebookresearch/segment-anything-2
They have a nice demo:
https://sam2.metademolab.com/
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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
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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.
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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.
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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.