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自動運転系エンジニア 機械学習 プログラマー テスラTesla Model3 SR+ 2021 ラーメン大好き宜しくお願いします！

[Ajit kumar @ajitcodes]

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 ...

[Raytar @Raytar]

Andrej Karpathy spent 8 years at OpenAI and Tesla.

Last week he put everything he knows into one free 2-hour lecture.

People pay $15k for bootcamps that teach half of this.

You probably don't have 2 hours right now. Don't lose this in the feed.

Watch it. Then read the guide below and build your first loop.

[Bilim ve Teknoloji @bilimvetekno847]

Stanford, Claude ve ChatGPT'nin çalışma mantığını içten dışa açıklayan ücretsiz dersi yayımladı.

Çoğu insan potansiyelinin yüzde 90'ını boşa harcarken, Stanford bu bilgiyi 1 saat 44 dakikada öğretiyor.

Kaybetmemek için favorilere ekleyin.

[Morlex @0xMorlex]

Andrej Karpathy explained the 5 shifts that turned LLMs from chatbots into agentic systems:

00:00 - Memory turns chat into personal AI
6:41- Multimodal AI can read the world
16:58 - Thinking models solve harder tasks
24:51 - Search makes LLMs live
30:58 - Tools turn LLMs into workers

This is not another video about “prompt engineering.”

It is a 40-minute roadmap for the next AI workflow: memory / vision / reasoning / search / tools.

Watch today, then read the article below on how to turn LLMs into self-improving agent loops.

[Dhairya Karekar @dkare1009]

Stanford's ALL FREE Courses (AI & ML):

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

Course links inside 👇

[ガガロット @gagarot200]

Anthropicの主任エンジニアが約3.2億円で
構築していた社内の「Obsidian Brain」を
流出させて一般公開されてる
オラの環境に入れたらマジでクッソ変わったw
👇
公開された全ナレッジはリプで解説

[はやたす@著書【Pythonブートキャンプ】 @hayatasuuu]

スタンフォード大学の教授が
「機械学習の総合講座」をCourseraで
動画講義とプログラミング演習付きで公開されてる。

・機械学習の基本概念
・教師あり学習（線形回帰・分類）
・ニューラルネットワーク入門
・決定木とアンサンブル
・教師なし学習（クラスタリング）
・レコメンドシステム
・強化学習の入門

MLを動画で体系的に学びたい人は必見です。

https://t.co/TflOzoRJks

[はやたす@著書【Pythonブートキャンプ】 @hayatasuuu]

Googleが「機械学習集中講座」を
Web上で完全無料公開してる。

・機械学習の基礎概念
・線形回帰・分類
・データ前処理とデータ準備
・ニューラルネットワーク入門
・埋め込みベクトル（Embeddings）
・実務でのML運用パターン
・演習付きイン���ラクティブ教材

MLをGoogleの公式カリキュラムで学びたい人は必見です。

https://t.co/kyA0vvuAER

[Vikas gupta @vicky_grok]

Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.

[みやさかしんや@Python/AI/DX @miyashin_prg]

東大が機械学習の講義動画を公開🤗文系の生徒も出席しているようで教師あり学習とは何か？から丁寧に解説されていて初心者にも優しい内容🤗✨

Ⅰ．教師あり学習
　　１．最小二乗法
　　２．過学習と正則化
　　３．交差検証
　　４．正則化付き経験リスク最小化
　　５．カーネル法
Ⅱ．教師なし学習
　　１．ハードクラスタリング問題
　　２．ソフトクラスタリング問題
　　３．次元削減問題
Ⅲ．ベイズ推論
　　１．各問題の確率論的定式化
　　２．推定理論
Ⅳ．凸最適化
　　１．凸関数
　　２．双対問���
　　３．最適化法など
講義動画へのリンクはリプ欄にて🤗✨

[Kirk Borne @KirkDBorne]

PDF copies available…

[Kirk Borne @KirkDBorne]

Download 656-page PDF ebook…

"Introduction to Theoretical Computer Science"

Online textbook used for Harvard CS 121

➡️ https://t.co/iMv4HWaPHV ⬅️
—————
#ComputerScience #Algorithms #Mathematics

[徐伦 @Jolyne_AI]

推荐一本 GitHub 上的免费机器学习与 AI 学习书：Machine Learning Q and AI。

全书围绕 30 个机器学习与人工智能的核心问题展开，从神经网络到模型部署，把关键知识点一次讲清。

GitHub：https://t.co/yNSOQ1Sf7o

在线阅读：https://t.co/3Ezh9Oynzu

内容按 5 条主线系统梳理：神经网络、深度学习、计算机视觉、自然语言处理、模型部署，学起来更有路径、更不容易漏重点。

无需下载，打开就能免费读，适合从入门到进阶、再到实战落地的各层级开发者。

[Model3 Life! @Model3Life]

むしろテスラに欲しいのよ 笑

[Swapna Kumar Panda @swapnakpanda]

Cambridge's Books on AI & ML (FREE DOWNLOAD):

1. Understanding Machine Learning
https://t.co/I2wE09jcHV

2. Mathematics for Machine Learning
https://t.co/EtVquoSoXx

3. Mathematical Analysis of ML Algorithms
https://t.co/YhmeTHQyyC

4. The principles of Deep Learning Theory
https://t.co/j7LVkTeKV6

5. Machine Learning with Neural Networks
https://t.co/d3msysZ7F6

6. Deep Learning on Graphs
https://t.co/306Yuo4uv7

7. Algorithmic Aspects of Machine Learning
https://t.co/NWCrOX7ZzE

8. Probability: Theory and Examples
https://t.co/uuvRS9hNW1

9. Elementary Probability for Applications
https://t.co/2acx1cxiri

10. Advanced Data Analysis
https://t.co/C48Gyipzwd

[EDU @edusoyy]

🚨ANTHROPIC ACABA DE PUBLICAR EL MANUAL PARA MONTAR UNA EMPRESA SIN EMPLEADOS

>CEO: 1 persona.
>empleados: agentes de claude

dura 30 minutos y es gratis

Guarda esto en favoritos para que no lo pierdas.

[はやたす@著��【Pythonブートキャンプ】 @hayatasuuu]

名古屋大学の「実践データサイエンティスト育成プログラム」がeラーニング＋実世界データ演習で体系化されてる。

・データサイエンス基礎理論
・Python／Rによる実装
・機械学習・ディープラーニング
・実世界データの前処理・可視化
・企業提供データを使ったPBL（実データ演習）
・モビリティ・製造業のDX事例
・チームでの課題解決演習

「講義で学んで、実データで手を動かす」の
両輪で回せるのが最大の強み。

https://t.co/3n2IYM49YQ

[Millie Marconi @MillieMarconnni]

A guy who was the number one ranked machine learning competitor on Earth, twice, looked at how universities teach AI and decided they had the entire thing backwards.

So he built a free course that has turned more people into working AI practitioners than most graduate programs.

Jeremy Howard was the guy who made the course and it is called Practical Deep Learning for Coders.

Here is the argument that drives the whole thing.

Universities teach AI top-down. First you sit through linear algebra. Then calculus. Then probability. Then, maybe, a year later, you are finally allowed to touch a model. Howard watched this approach destroy motivated people. Most never made it to the part where it gets interesting. The math wall killed them first.

He thinks that is exactly wrong. His view is that you do not teach someone baseball by drilling the physics of a curveball for a year before letting them hold a bat. You let them play, then explain the physics once they care.

So his course inverts it. In the very first lesson, before any heavy theory, you train a working image classifier that actually runs. You build something real on day one. The theory comes later, pulled in piece by piece, exactly when you finally need it to go deeper.

Harvard Business Review said fast AI can take motivated students all the way to building industrial-grade AI systems.

The whole course is free. No paywall, no signup tricks.

It assumes you can code a little and remember some high school math. That's the bar.

The people who actually break into AI almost never start with the equations.

https://t.co/ea9S8yk1Cl

[徐伦 @Jolyne_AI]

微软开源了一套 AI 智能体入门课程：AI Agents for Beginners，全套 12 讲带你从零搭建自己的第一个智能体。

GitHub：https://t.co/J5eoGU4am8

课程内容覆盖智能体开发的核心基础：

- AI 智能体简介与应用场景
- 主流智能体框架探索（Microsoft Agent Framework、Azure AI Foundry Agent Service V2 等）
- 智能体设��模式（Tool Use、Agentic RAG、Planning、Multi-Agent 等）
- 工具调用与可信智能体构建
- 内存管理与自主决策
- LangGraph、Semantic Kernel、AutoGen 等框架实战
- RAG 应用与模板化设计
- 智能体部署与企业级应用

每讲配有详细文档、视频讲解和可直接运行的 Python 示例代码，全部基于 Microsoft Agent Framework 和 Azure AI Foundry 构建，部分示例也支持 MiniMax 等 OpenAI 兼容服务。

课程已支持 50+ 种语言自动翻译，包含简体中文。

适合想系统学习智能体开发、理解主流框架和设计模式的开发者。

[Anatoli Kopadze @AnatoliKopadze]

Anthropic engineer:

"You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day."

In 45 minutes he shows exactly how to do it from scratch, step by step.

Most people are still doing all of this by hand.

Watch the session, then save the guide below.