kayunawel

PhD Researcher UNAB | Master in Data Science | Industrial Engineer | Music Teacher | Heavy Equipment Technician | STEAM Professor

[Praveen Kumar Verma @Alacritic_Super]

Looking for one resource that covers Maths, Computer Science, and AI from first principles?

Check out the Maths, CS & AI Compendium by Henry Ndubuaku. 📚

It brings together the foundations behind modern AI in one open-source textbook.

📖 Topics covered:

✅ Vectors & Linear Algebra
✅ Matrices & Decompositions
✅ Calculus & Optimization
✅ Statistics & Probability
✅ Machine Learning
✅ NLP & Transformers
✅ Computer Vision
✅ Audio & Speech AI
✅ Multimodal Learning
✅ Autonomous Systems & Robotics
✅ Graph Neural Networks
✅ Operating Systems & Computer Architecture
✅ Data Structures & Algorithms
✅ Production Software Engineering
✅ SIMD, GPU Programming & CUDA
✅ AI Inference Optimization
✅ ML Systems Design

📚 Read Online: https://t.co/A1aU594rb4

[David Moreira @___Davidmoreira]

Primer evento oficial de la comunidad n8n en Chile 🚀 Workshop presencial y gratuito de automatización para AEC: incidencias, registros y coordinación con n8n, con la IA como apoyo del criterio profesional.

USM San Joaquín · 30 de julio · 19:00-22:00h · cupos limitados.

[kayunawel @gonz4GTR]

@NadzuAI AI

[NadzAI @NadzuAI]

🚨 𝗔𝗜 𝗦𝗞𝗜𝗟𝗟𝗦 𝗧𝗛𝗔𝗧 𝗖𝗢𝗨𝗟𝗗 𝗠𝗔𝗞𝗘 𝗬𝗢𝗨 𝗠𝗢𝗡𝗘𝗬 𝗜𝗡 2026 — NOW FREE 🚨

Thousands are learning high-income AI skills online…

But most paid courses cost hundreds of dollars 💸

So I collected the best AI resources and made them FREE for a limited time 🔥

Inside you’ll learn:

✅ ChatGPT + Claude + Gemini + Grok
✅ Prompt Engineering
✅ AI Content Creation
✅ Python Programming
✅ Machine Learning Basics
✅ Data Science & Analytics
✅ Automation Tools
✅ Cyber Security Fundamentals
✅ Freelancing with AI
✅ How to make money using AI

💻 Beginner Friendly
🌍 Learn From Anywhere
⏰ Study At Your Own Pace
💰 No Degree Needed

⚠️ Free access is only available for a limited time

To Get Access 👇

1️⃣ Follow me
2️⃣ Like + Repost ♻️
3️⃣ Comment “AI”

I’ll send everything to your DM 📩

[kayunawel @gonz4GTR]

@AkterBrist39045 Gemini

[kayunawel @gonz4GTR]

@iam_chonchol drive

[Alamin @iam_chonchol]

All Paid Courses (Free for First 4500 People)

𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1)
1. Artificial Intelligence
2. Machine Learning
3. Prompt Engineering
4. Claude,Chatgpt,Grok
5. Data Analytics
6. AWS Certified
7. Data Science
8. BIG DATA
9. Python
10. Ethical Hacking

(72 Hours only )

Like + RT + comment ' Drive '

Must Follow me so I can DM you.

[Kirk Borne @KirkDBorne]

Principles of Deep Learning Theory—Theoretical & Mathematical Foundations:
471-page PDF Draft: https://t.co/1y5opTWW5d
-or-
Buy the newer edition: https://t.co/1PUSo1VwNG

[Didier 'Dirac's ghost' Gaulin @DiracGhost]

I wasn't planning to post today, but a family member who just started studying applied mathematics, asked me for some material to check out, and recommended 'A First Course in Monte Carlo Methods' by Sanz-Alonso and Al-Ghattas, a fantastic primer in 150 pages, available on arXiv.

Engineers, computer scientists and applied mathematicians will enjoy this one, I am sure.

🔗👇👇

[鸟哥 | 蓝鸟会🕊️ @NFTCPS]

剑桥这波 AI & ML 经典教材全免费放出来，PDF 直接下，一分钱不花。我知道你们的收藏夹里躺着几百个「以后再看」，但这次是真该看的那种。想入门机器学习又不想被割韭菜买天价课的，这十本啃完，基本就够你打地基了。

按顺序给你捋一遍，从入门到进阶，别一上来就啃最硬的那本：

1️⃣ 机器学习理解，讲理论和算法，入门首选
🔗 https://t.co/Kv57CnV1CM

2️⃣ 机器学习数学基础，数学不行的先补这本
🔗 https://t.co/n0s3mjyT88

3️⃣ 机器学习算法的数学分析
🔗 https://t.co/mSUwVGmViy

4️⃣ 深度学习理论原理
🔗 https://t.co/qjpmPje1Nj

5️⃣ 神经网络与机器学习
🔗 https://t.co/669BANjcDV

6️⃣ 图深度学习
🔗 https://t.co/wXJYjZcQ3Y

7️⃣ 机器学习的算法视角
🔗 https://t.co/EsMhr7mAKK

8️⃣ 概率论：理论与实例
🔗 https://t.co/Rs8fzUAf7C

9️⃣ 应用概率基础
🔗 https://t.co/5gsXoVZr4Z

🔟 高级数据分析
🔗 https://t.co/vv3REXP6hA

说句大实话，这些书难度不低，别指望躺着刷完。但只要你真下决心啃两三本，比在群里看别人吹 AI 强太多了。先存下来，然后今晚就打开第一本，别又拖成明天。

[Math Cafe @Riazi_Cafe_en]

MIT's "Mathematics for Computer Science".

PDF available at: https://t.co/lzB0hHGsEs

[Param @ParamSiddh]

Best YouTube Channels To Learn AI in 2026 (No BS)

1. Fundamentals – 3Blue1Brown
2. Deep Learning – Andrej Karpathy
3. AI Research – Yannic Kilcher
4. Practical AI – AssemblyAI
5. LLMs – AI Explained
6. ML Theory – StatQuest
7. Papers Simplified – Two Minute Papers
8. GenAI – Matthew Berman
9. AI Agents – Nicholas Renotte
10. Applied ML – Krish Naik
11. PyTorch – Aladdin Persson
12. Math for ML – Serrano Academy
13. Industry Insights – Lex Fridman
14. Real-world AI – DeepLearningAI

[Adam @Adamaestr0_]

Seedance 2.0 es una máquina de hacer dinero.

Saber usarla permite generar ingresos en YouTube, TikTok, Instagram...

Por eso, creé la guía definitiva de Seedance 2.0 con prompts, tips, monetización… lo tiene todo.

¡GRATIS solo durante 24h!

Solo:
1. Dale like
2. Comenta "SEEDANCE"
3. Sígueme para recibir el DM

[Dan Kornas @DanKornas]

Stop learning ML from random tabs.

Machine-Learning-Library is a curated GitHub list of learning resources for machine learning, deep learning, AI, reinforcement learning, and the math behind them.

It helps you build a cleaner learning path by grouping courses, books, coding tutorials, interactive demos, and foundations into scan-friendly sections instead of leaving everything scattered across bookmarks.

Key features:

• Deep learning map – playlists/courses plus books and blogs for neural networks, NLP, transformers, and generative models
• Coding resources – tutorials and notebooks for PyTorch, TensorFlow, LLMs, Papers with Code, and hands-on implementation work
• Core ML section – courses and visual explainers covering machine learning, statistics, decision trees, and AI fundamentals
• Math foundations – dedicated sections for calculus/optimization, probability, and linear algebra
• Interactive demos – visual resources for transformers, convolutional neural networks, and prompt engineering

Free public GitHub repo.

Link in the reply 👇

[Kirk Borne @KirkDBorne]

[461-page PDF eBook] Physics-based Deep Learning: https://t.co/78p3ofpb8t
+
Learn more at: https://t.co/xcJNNXs2Qe

——————
#AI #MachineLearning #ML #DataScience #NeuralNetworks #Algorithms #Mathematics #DataScientist

[kayunawel @gonz4GTR]

@heyalexmoore Drive

[Alex Moore @heyalexmoore]

All Paid Courses (Free for First 4500 People).

𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1)
1. Artificial Intelligence
2. Machine Learning
3. Prompt Engineering
4. Claude,Chatgpt,Grok
5. Data Analytics
6. AWS Certified
7. Data Science
8. BIG DATA
9. Python
10. Ethical Hacking

(72 Hours only )

Like + RT + comment ' Drive '

Must Follow me so I can DM you.

[Dan Kornas @DanKornas]

If machine learning only clicks when you build the pieces yourself, this is a useful repo to keep around.

Build your own X - Machine Learning is a public build-from-scratch machine learning tutorial index for learners and builders who want implementation practice.

It helps you move from reading algorithms to coding them by organizing ML topics into categories and linking several NumPy examples for core algorithms.

Key features:
• Build-from-scratch roadmap – starts at linear/logistic regression and KNN, then expands into deep learning and LLM topics
• Core Python examples – includes NumPy code for regression, KNN, loss functions, and activation functions
• Category navigation – groups ideas across recommendation systems, computer vision, NLP, forecasting, anomaly detection, and more
• Implementation-first learning – matches the README’s goal of building ML pieces from scratch
• Ongoing tutorial list – README says it will keep adding new tutorials

It’s open-source (Apache-2.0 license).

Link in the reply 👇

[Cliff Pickover @pickover]

FREE Math Book.

"Understanding Linear Algebra" by Austin. Introduces many applications so that readers appreciate how linear algebra plays a vital role in our society. Computer animation, image compression, Google PageRank, least squares, principal component analysis, and singular value decompositions. The book is used in over 100 institutions, from R1s to community colleges and high schools.
Link: https://t.co/u3dhK3N2sI

[Python Coding @clcoding]

A Mathematical Introduction to Data Science with Python

Detailed Explanation: https://t.co/1DcLte7zEN