与「algorithm」相关的搜索结果

There are good people all across our nation. Despite a media ecosystem and social media algorithms that so often amplify hate, outrage, division, and cruelty, there are countless acts of kindness happening every single day. Be the reason someone believes in the goodness of people. Lift up the quiet acts of compassion, the daring deeds of generosity, and the humble heroism of ordinary people helping one another. And above all, be kind. op: @sparksdafello

Hiwonder SO-ARM101 doesn't need a programmer – it needs a teacher. That's you. Move the leader arm, and the follower arm picks up every detail. Learn more 👉 #huggingface# #LeRobot# #modeltraining# #ImitationLearning# #ailearning# #algorithm# #opensource#

There's a surprising amount of science behind the packaging your order arrives in. At Amazon's fulfillment centers, machine learning algorithms help analyze millions of products to figure out the best way to package each one. They determine what size, what material, and whether it even needs added packaging at all. The result: protecting products with less waste, one package at a time. Take a look at how it works. ⬇️

With AI-RAN, we deliver twice the #spectralefficiency# by 2028. Combining #anyRAN# software, advanced #AI# algorithms and accelerated computing from partners like @NVIDIA, we enable software-speed improvements in RAN. Discover more:

推特必装的 10 个 Skills 推荐 1、last30days 把过去 30 天里 X、Reddit、Hacker News、YouTube 真正在讨论的东西抓回来，还会参考真实互动量排序。找热点、挖选题特别省事。 2、xint 一个 X 情报台：搜推文、查账号、拆互动、看情绪。不是凭感觉教你运营，而是直接读 X 上的数据。 3、customer-research 把评论、反馈和用户原话，整理成受众真正关心的痛点、需求和反对意见。以后找选题，不用再靠自己脑补。 4、social-content 从内容支柱、选题、钩子，到改成 X 版本、安排内容日历，基本等于给账号配了一个内容总编。 5、copy-editing 专门负责改稿，不会一上来把整条推文重写成 AI 味。砍废话、理顺表达，同时尽量保住你原来的判断和语气。 6、Firecrawl 把官网、新闻原文、GitHub 和产品文档抓干净。写热点前先让它查一遍，少拿二手消息当事实。 7、twitter-algorithm-optimizer 从受众、主题、互动信号等角度检查推文为什么可能没人看。里面部分算法资料比较旧，适合当发帖前检查表，别当最新算法内幕。 8、chart-visualization 自动判断数据适合做条形图、折线图还是对比图，再生成可视化。数据型推文终于不用自己慢慢搓图。 9、x-twitter 直接连接 X API，搜索、书签、趋势、账号分析和发帖都能管。权限比较大，第一次装建议先只开读取权限。 10、Postiz Agent 负责上传图片、创建单条推文或 Thread、保存草稿和定时发布。需要 Postiz 账号或自己部署，正式发布前最好保留人工确认。

🟡 We ship every day. You pick tomorrow's. What should land next on [ Poll options — X allows 4 ] • Staking dashboard • Agent builder (deploy TANO-style agents) • 1-click rent + wallet • Provider onboarding flow Vote 👇 We build what wins. (WHY: a poll turns followers into participants, signals your roadmap is community-driven, and every vote is an interaction the algorithm rewards. Reply to voters to double the reach.)

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

my spicy theory is that chinese ai labs keep winning because of culture, not talent or resources. chinese labs still have a deeply hands-on engineering culture. deepseek and kimi are flat organizations where science and engineering are fused: the same people move between algorithms, data, and infra, doing whatever it takes to make the model work. but sf tech bros have decided that “researcher” is the high-status title while infra is merely support work. every new sf ai startup calls itself a “lab,” every ambitious engineer quietly upgrades their title to “research engineer,” and the infra work is left to whoever failed to escape it. but at frontier scale, infra determines experiment velocity, and experiment velocity determines research output. at frontier scale, infra **is** research.

Tim Roughgarden, mathematician at Institute for Advanced Study and head of research at a16z Crypto, on quantum computing, blockchain as a laboratory for social science, and AI transforming mathematics right now the way it transformed coding six months ago. 1:35 — The story from Turing to 2026 3:43 — Quantum was "25 years away" for 25 years, then something changed 4:25 — Q-Day prediction + Shor's algorithm + how to spot a quantum agenda 6:37 — Positive quantum applications + we're not doomed on cryptography 9:16 — Post-quantum migration is a coordination problem 12:22 — Blockchain as a laboratory for social science 15:50 — The sociology big bang analogy 17:37 — Computation is a law of nature 19:56 — AI is transforming mathematics right now 25:54 — Start with what you want to build @Tim_Roughgarden

#AI# doesn't run on algorithms alone. It runs on data. Your phone translates a menu with one camera point. Your car reacts to a hazard before you do. Your email suggests the perfect reply at a tap. Memory and storage make it happen. 👉 #IntelligenceAccelerated#