abcbbb

[abcbbb @abcbbb556]

@Lovekurt0511 @liuyuxin89064 民国已经在49年4月灭亡了！

[sunick @Serantych]

Kimi's CEO Zhilin Yang:

"everyone's trying to build one smarter agent

many agent hits a wall fast, so instead of making it smarter we just made more - one boss, a thousand workers."

in a 39-minute talk he explains why one agent won't get you to real work.

agent swarms + long context + RL on every sub-agent - that's the fix.

bookmark this ↓

[UT San Antonio @UTSA]

The future of AI may be built on failure, not perfection. 🤖

Professor Yongcan Cao from @UTSA_CEID is developing a new approach to AI training that uses past mistakes to create smarter autonomous systems. Supported by a $500K @USNavyResearch grant, the research has applications across robotics, healthcare, and beyond.

Learn more: https://t.co/z1cUL9mwIO

[abcbbb @abcbbb556]

@spurs @okcthunder @NBAonNBC @1200WOAI @kxtn1350 @HEB @Ledger Go spurs go 210

[Dan Kornas @DanKornas]

Stop discovering ML Python libraries one random tutorial at a time

Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem.

It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers.

Key features:

• 920-project index – a large scan-friendly map of open-source ML Python projects
• 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more
• Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals
• Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies
• Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits

It’s open-source (CC BY-SA 4.0 license).

Link in the reply 👇

[Rony @Ronycoder]

This 1 hour Stanford lecture on Markov Decision Processes will teach you more about the math behind systematic trading decisions than a 3 month internship at Jane Street or JPMorgan.

Bookmark & replace one movie today with this lecture, no matter what.

[tut_ml @tut_ml]

Best CNN Courses- https://t.co/ep7RM3P809

[知识分享官 @knowledgefxg]

学机器学习最痛苦的事情是什么？是对着一堆公式发呆，死记硬背，项目作者把神经网络、逻辑回归等这些经典算法，从数学第一原理开始推导，一步一步写成 Jupyter Notebook，然后把整个训练过程可视化出来，让你在学习的时候有个直观的感觉。
如果你正在学 ML，这个项目值得你花时间好好过一遍。

[Kirk Borne @KirkDBorne]

Linear Algebra and Optimization for Machine Learning [516-page textbook]: https://t.co/62bWLNwzd4
—————
#DataScience #DataScientist #ML #Mathematics #ORMS

[柴郡🔔｜Crypto+AI Plus @0xCheshire]

与其看一小��的抖音，不如看看这场 2 小时的斯坦福讲座。

关于像 ChatGPT 和 Claude 这样的 LLM 是如何构建的，它能教给你的东西，比大多数在顶尖 AI 公司工作的人整个职业生涯中所学到的都要多。

[Joruno @wsl8297]

加州大学开放课程《大语言模型的强化学习》，用“理论 + 实战”的方式，把 AI 训练的关键技术从零到一讲透，帮你系统建立从强化学习到 LLM 训练的完整框架。

课程内容覆盖全面，配套资源齐全：讲座幻灯片、完整视频、实践练习一应俱全，学完就能上手做。

课程地址：https://t.co/RymrTNttb6

你将学到：

- 深度强化学习核心：MDP、策略梯度、A3C、PPO 等关键算法
- 大语言模型基础：NLP、语言建模、RNN 等入门与脉络
- RLHF 全流程拆解：基于人类反馈的训练方法与落地思路
- 可验证奖励强化学习：面向更安全、更可靠的训练范式
- 动手实践：Jupyter 代码示例 + 课后作业，边学边练

课程由 UCLA 数学系助理教授主讲，YouTube 提供全套视频，内容扎实，适合想把“RL + LLM 训练”真正学明白的人。

[Swapna Kumar Panda @swapnakpanda]

Stanford's ALL FREE Courses on AI & ML:

❯ CS221 - Artificial Intelligence
❯ CS229 - Machine Learning
❯ CS230 - Deep Learning
❯ CS234 - Reinforcement Learning
❯ CS224N - NLP with Deep Learning
❯ CS336 - LLM from Scratch

13 Course links inside:

[Roan @RohOnChain]

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch. Stanford taught the entire thing in 1 hour lecture & released it for free.

Bookmark & watch this today before someone takes it down.

[Xiuyu Li @sheriyuo]

写的非常详细的入门教程

RL 很恶心的点就在于你什么都要会：pretrain、inference、theory、infra

现在我也有了可以推荐给别人入门的读物，比我当时东看一点西看一点好太多了🥺

很多现代 LLM 的教学其实跳过了相当一部分的基础知识，比如 CNN、Q-Learning 这一类经典内容。我感觉我也是填鸭式地在学习这些内容，需要耐下心来慢慢地去看那些最旧最老最传统的东西

很感谢苏剑林的 blog 带我入门，我也想能写出一些带别人入门的东西，只不过即使是用 LLM 辅助写 notes 也相当折磨人，然后知乎上还没什么人看

当然还是有很多人喜欢看我写的垃圾 notes 的，还被行业杰青大佬订阅了，这种事跟 paper 被人关注了一样开心

Website: https://t.co/TR35oZGB7J

[Gappy (Giuseppe Paleologo) @__paleologo]

Reading a paper on why Deep Learning works. Short answer: PAC-Bayes.

[Math Cafe @Riazi_Cafe_en]

Princeton's "Optimization for Machine Learning" lecture notes
by Elad Hazan

PDF: https://t.co/LMToEkl58L

[PyQuant News 🐍 @pyquantnews]

(Finally) figure out how neural networks work:

[PyQuant News 🐍 @pyquantnews]

New to Pandas?

Here's a cheat sheet you can download (free):

[UT San Antonio @UTSA]

Nothing says school spirit like the UTSA fight song!

Who’s ready for game day?

@UTSASOSA

[abcbbb @abcbbb556]

God bless you! Dear Frank.

[TMZ @TMZ]

Judge Frank Caprio, who went viral for making compassionate decisions in his courtroom, has died at 88 💔🕊️