🚀 RESEARCH & DIGITAL SOLUTIONS — YOUR PARTNER IN SUCCESS!
Are you a researcher, student, business owner, or developer looking for professional support? We are here to help you turn your ideas into real results! 💡

🔬 1. Scientific Research & Academic Publishing
Professional review and evaluation of scientific manuscripts before submission.
Guidance on journal selection and publication strategies.
Expert advice to improve research quality and presentation.
Support for researchers throughout the academic publishing process.

💻 2. Website & Software Development
Professional website design and development for businesses and individuals.
Custom electronic systems tailored to your needs.
Technical assistance for students and academic projects.

⚙️ 3. API & User Interface (UI) Services
API development and integration.
Modern, responsive, and user-friendly interface design.
Seamless integration between applications and systems.

📊 4. Real-World & Synthetic Datasets
Providing real-world datasets for research and analysis.
Creating customized synthetic datasets according to your requirements.
Data support for scientific research, machine learning, and AI projects.

🤝 5. FREE Discussion & Consultation
Discuss your research ideas and technical projects with us.
Get initial guidance and recommendations.
Explore the best solutions for your academic or professional needs.

🌟 WHY CHOOSE US?
✅ Research and technology expertise in one place.
✅ Tailored solutions based on your needs.
✅ Professional guidance and practical support.
✅ Free initial discussion and consultation.

📩 CONTACT US ON TELEGRAM
👨‍💻 Team Admin & Support: @Omidyzd62
💬 Feel free to contact us to discuss your ideas, ask questions, or explore how we can help!
Your Research. Your Ideas. Our Expertise. Your Success. 🎯

#ResearchSupport #AcademicPublishing #ScientificResearch #WebDevelopment #SoftwareDevelopment #API #UIUX #Datasets #MachineLearning #ArtificialIntelligence #StudentSupport

🔖 A Comprehensive Resource for Machine Learning Practice

The platform offers:

🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
🫡 Deep Learning, NLP, and Computer Vision;
🫡 Labs with real datasets;
🫡 Learning paths and interview preparation;
🫡 Projects using GPT, RL, and CUDA.

Almost everything can be accessed directly in your browser, and basic tasks are available for free.

⛓️ Link to the platform
https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

#MachineLearning #DeepLearning #DataScience #AI #Coding #Tech

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

"Matrix Differential Calculus for Machine Learning and Beyond" is a free course of lecture materials from MIT, focusing on the mathematics of derivatives of vectors and matrices.

The materials cover gradients, Jacobians, derivatives of matrix functions, differentials, derivatives of matrix products, inverse matrices, and determinants, Kronecker products, optimization, and automatic differentiation. It also explores the application of these methods in machine learning.

In my opinion, this is a valuable resource for those who are already familiar with the basics of linear algebra and mathematical analysis and want to understand how differentiation is generalized to vectors and matrices and applied in modern optimization problems.

https://ocw.mit.edu/courses/18-s096-matrix-calculus-for-machine-learning-and-beyond-january-iap-2023/mit18_s096iap23_lec_full.pdf

https://t.iss.one/MachineLearning9

You can literally enter a sentence and see how the Transformer processes it.

This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process:

Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token.

You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation.

If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this.

https://poloclub.github.io/transformer-explainer/

https://t.iss.one/DataAnalyticsX

https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8

Follow me to more posts and useful content

📚 7 Best Websites to Learn Computer Science Subjects for Free

More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Agentic AI is the future.

If you want to become god-level with AI agents, save these 10 GitHub repos:

1 Awesome LLM Apps

🔗 https://github.com/Shubhamsaboo/awesome-llm-apps

2 Generative AI for Beginners

🔗 https://github.com/microsoft/generative-ai-for-beginners

3 LLM Course

🔗 https://github.com/mlabonne/llm-course

4 Prompt Engineering Guide

🔗 https://github.com/dair-ai/Prompt-Engineering-Guide

5 AI Agents for Beginners

🔗 https://github.com/microsoft/ai-agents-for-beginners

6 AI Agents Projects

🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects

7 Hugging Face Agents Course

🔗 https://github.com/huggingface/agents-course

8 Awesome AI Agents

🔗 https://github.com/e2b-dev/awesome-ai-agents

9 GenAI Agents

🔗 https://github.com/NirDiamant/GenAI_Agents

10 Agents Towards Production

🔗 https://github.com/NirDiamant/agents-towards-production

Anything else you'd add?

🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup

If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.

📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers
→ https://bit.ly/4yrk1Ti

🪜 Online Free Course — structured path, works as a refresher before exams
→ https://bit.ly/4hUprRP

☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks
→ https://bit.ly/4yt1iXy

📊 Cloud Study Guide — cheat-sheet style, handy for interview prep
→ https://bit.ly/4el4Eod

🧠 Free Mock Exam — test yourself before booking the real thing
→ https://bit.ly/4sbWlkl

📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪

🌐 Community: https://chat.whatsapp.com/CS7VOvQCLZG5BJPazBGm1v
📲 Personalized help: https://wa.link/xd2fu6
#Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers

🔖 637 interactive modules on ML and AI

On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
🫡 Query, Key, and Value;
🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.

Change the input data and immediately see the results of the calculations.

⛓ Link to the tutorial
https://vizlearn.in/

The 25 Best Free Resources for Learning AI in 2026

Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.

Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

🚨 SURPRISE ALERT! 🚨

Stop paying full price on Udemy. Seriously. 💸

I built a bot that hunts down 100% FREE Udemy coupons 24/7 — while you sleep, eat, or scroll. 🎯

Here's the magic:

📚 Mini App catalog — every active free coupon in one place
🔔 Auto-push — new courses land straight in your chat
📢 Live channel — never miss a deal

Why it matters?
Most people pay $200+ for courses you can grab for $0 — if you know where to look. Now you have a bot that does the looking for you. ⚡

🎓 Try it now: https://t.iss.one/UdemySybot?start=adsun

Your future self (and your wallet) will thank you. 💜

🗂 500+ Cheat Sheets on Machine Learning

This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.

Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.

⛓ You can find it here
https://github.com/gurovic/MLCheatSheets

#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Write NumPy code in the familiar way, and then run it on GPUs and TPUs. Automatic differentiation and data batching will be handled automatically, without requiring any extra effort from you.

- Automatically compute derivatives of standard Python and NumPy functions.
- Compile pure functions using jit to run them on hardware accelerators.
- Combine grad, jit, and vmap in any order and scale your computations across multiple devices.

https://github.com/jax-ml/jax

https://t.iss.one/MachineLearning9

"Advanced Calculus" is a free textbook, over 500 pages long, from the mathematics department of Harvard University, dedicated to an in-depth study of mathematical analysis.

It covers topics such as normed vector spaces, compactness and completeness, multi-dimensional mathematical analysis, integration, differential equations, multilinear algebra, differentiable manifolds, exterior calculus, potential theory, classical mechanics, and much more.

This is one of the most interesting resources I've shared in recent years. The book is definitely worth adding to your personal library of high-quality, freely available mathematics textbooks.

https://people.math.harvard.edu/~shlomo/docs/Advanced_Calculus.pdf

https://t.iss.one/MachineLearning9

pandas — The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet.

If the field is short, cut it to: A 31-page practical guide to pandas — 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0.

https://t.iss.one/MachineLearning9

NumPy looks like a calculator and behaves like a memory model. 🧮

Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses.

15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. 🚀

Arrays, shapes and the machinery under the math:
https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true