#cplusplus#android_auto#android_automotive#headunit#wireless_android_auto
You can turn your car’s wired Android Auto into wireless using a Raspberry Pi and a special software called Wireless Android Auto Dongle. This DIY adapter connects your phone to the car wirelessly via Bluetooth and Wi-Fi, without needing extra apps on your phone. It works with several Raspberry Pi models and boots up quickly, connecting in under 30 seconds. After the first setup, it automatically connects every time you start your car, making your Android Auto experience seamless and cable-free. This saves you from plugging in your phone every time and keeps your car’s infotainment system working smoothly[1][2][3].
https://github.com/nisargjhaveri/WirelessAndroidAutoDongle
#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.