Exploding Gradients
@ExplodingGrads
Training deep nets until they blow up 💥 Gradients explode, losses skyrocket, memes incoming. ML / DL / AI chaos served fresh daily.
Thinking of starting a nerd club — we pick a paper, roast it, rebuild it, and maybe outsmart the authors while we’re at it. Who’s in? 👀 #ResearchCommunity #AI #ML #DeepLearning #OpenScience #Reproducibility #TechTwitter
1 voto · Resultados finales
im crying someone bought a domain just to do this
Hello @Dell @HP @ASUS @Lenovo, apparently slow and bulky Windows deliberately only ships to India. Abroad: FreeDOS. Solution? India needs its own laptops: FreeDOS by default, no bloat, full control. Choice isn’t optional. 🇮🇳 #FreeDOS #Linux
When your Deep Learning model trains slower than a snail 🐌, don’t just blame model size or complexity. Run the holy trinity: • nvidia-smi dmon (GPU) • htop (CPU) • iostat (disk) Profiling > superstition. #DL #MLOps #CUDA #Gpu
The null relation ∅ is special: • Subset of its inverse ✅ •Subset of reflexive ✅ •Subset of irreflexive ✅ •Subset of symmetric ✅ •Subset of antisymmetric ✅ Why? Because the empty set is a subset of every relation. #Math #Relations
A mathematical overview of Quantum Computing Basics.
In classical information, bits are either 0 or 1. In quantum information, qubits can be in superpositions — like 0 and 1 at once. But when you measure them, they "collapse" to one outcome. The math behind this is where the magic begins. #QuantumComputing
💡 In quantum information theory, a single qubit can represent infinitely many states — but you can only extract one bit of classical information from it. Quantum weirdness, meet information limits. #QuantumComputing #QuantumInformation
Learning quantum computing? John Watrous has you covered with a full 16-lesson course — videos + notes — on: 🔹 Quantum information theory 🔹 Algorithms (Shor, Grover) 🔹 Error correction (CSS, Toric, etc.) Free, rigorous, and crystal clear. Link: arxiv.org/abs/2507.11536
Neural nets learn functions by fitting smooth, low-frequency parts first, then detailed high-frequency ones — a phenomenon called spectral bias. Early stopping leverages this to reduce overfitting. Here’s a quick visual explainer! #ML #DL #bias
What’s really happening: Dropout ≈ variational Bayesian inference. It approximates a deep Gaussian process — giving you uncertainty for free!
Quantum ML doesn’t crash. It exists in a superposition of working and not working until you try to deploy it. #QuantumComputing #MLproblems
Classical ML: Takes hours to train. Quantum ML: Takes milliseconds to confuse you. #ML #AI #DL #QuantumJokes
Fei-Fei Li (@drfeifei) on limitations of LLMs. "There's no language out there in nature. You don't go out in nature and there's words written in the sky for you.. There is a 3D world that follows laws of physics." Language is purely generated signal.
“Exploding Gradients” isn’t a bug. It’s a feature. A very loud, destructive feature. 💣💥 Stick around for ML tips and disaster stories.
United States Tendencias
- 1. #warmertogether N/A
- 2. $BARRON 1,973 posts
- 3. Harvey Weinstein 2,563 posts
- 4. Diane Ladd 2,609 posts
- 5. Ben Shapiro 25.7K posts
- 6. #NXXT 2,504 posts
- 7. $PLTR 15.6K posts
- 8. Laura Dern 1,193 posts
- 9. Gold's Gym 46.6K posts
- 10. #maddiekowalski 3,590 posts
- 11. #CAVoteYesProp50 4,679 posts
- 12. University of Virginia 1,745 posts
- 13. iOS 26.1 2,982 posts
- 14. Cardinals 11.6K posts
- 15. #BestStockToBuy 1,114 posts
- 16. Standout 7,738 posts
- 17. Mumdumi 11.3K posts
- 18. Shannon Library 1,743 posts
- 19. Ndiaye 8,875 posts
- 20. Murray State 1,229 posts
Something went wrong.
Something went wrong.