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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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📌 DenseNet Paper Walkthrough: All Connected
🗂 Category: DEEP LEARNING
🕒 Date: 2026-04-03 | ⏱️ Read time: 20 min read
When we try to train a very deep neural network model, one issue that we…
#DataScience #AI #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2026-04-03 | ⏱️ Read time: 20 min read
When we try to train a very deep neural network model, one issue that we…
#DataScience #AI #Python
📌 I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-03 | ⏱️ Read time: 13 min read
Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search.
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-03 | ⏱️ Read time: 13 min read
Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search.
#DataScience #AI #Python
Forwarded from Machine Learning with Python
Selection for those who want to become a certified Claude architect
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• Cookbook (examples and practices):
https://github.com/anthropics/anthropic-cookbook
• Exam guide:
https://share.google/0eqIbebzRMUt8KTc8
• Practice questions:
http://claudecertifications.com
• MCP documentation:
http://modelcontextprotocol.io
API documentation:
http://docs.anthropic.com
Useful playbook:
https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view
Useful resources for preparation in one place 👇
• Registration for certification: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request
• Training (13 free courses):
https://anthropic.skilljar.com
• Cookbook (examples and practices):
https://github.com/anthropics/anthropic-cookbook
• Exam guide:
https://share.google/0eqIbebzRMUt8KTc8
• Practice questions:
http://claudecertifications.com
• MCP documentation:
http://modelcontextprotocol.io
API documentation:
http://docs.anthropic.com
Useful playbook:
https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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Admin: @HusseinSheikho
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
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📌 Building a Python Workflow That Catches Bugs Before Production
🗂 Category: PROGRAMMING
🕒 Date: 2026-04-04 | ⏱️ Read time: 17 min read
Using modern tooling to identify defects earlier in the software lifecycle.
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2026-04-04 | ⏱️ Read time: 17 min read
Using modern tooling to identify defects earlier in the software lifecycle.
#DataScience #AI #Python
❤2
📌 Building Robust Credit Scoring Models with Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-04 | ⏱️ Read time: 24 min read
A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring.
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-04 | ⏱️ Read time: 24 min read
A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring.
#DataScience #AI #Python
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Forwarded from Machine Learning with Python
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📌 A Data Scientist’s Take on the $599 MacBook Neo
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-05 | ⏱️ Read time: 7 min read
Why it doesn’t fit my workflow but still makes sense for beginners
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-05 | ⏱️ Read time: 7 min read
Why it doesn’t fit my workflow but still makes sense for beginners
#DataScience #AI #Python
📌 Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost
🗂 Category: LARGE LANGUAGE MODEL
🕒 Date: 2026-04-05 | ⏱️ Read time: 23 min read
A new way to build vector RAG—structure-aware and reasoning-capable
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODEL
🕒 Date: 2026-04-05 | ⏱️ Read time: 23 min read
A new way to build vector RAG—structure-aware and reasoning-capable
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📌 The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-06 | ⏱️ Read time: 12 min read
The geometric foundations you need to understand the dot product
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-06 | ⏱️ Read time: 12 min read
The geometric foundations you need to understand the dot product
#DataScience #AI #Python
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📌 How to Run Claude Code Agents in Parallel
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-04-06 | ⏱️ Read time: 11 min read
Learn how to apply coding agents in parallel to work more efficiently
#DataScience #AI #Python
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-04-06 | ⏱️ Read time: 11 min read
Learn how to apply coding agents in parallel to work more efficiently
#DataScience #AI #Python
📌 Behavior is the New Credential
🗂 Category: CYBERSECURITY
🕒 Date: 2026-04-06 | ⏱️ Read time: 7 min read
We are living through a paradigm shift in how we prove we are who we…
#DataScience #AI #Python
🗂 Category: CYBERSECURITY
🕒 Date: 2026-04-06 | ⏱️ Read time: 7 min read
We are living through a paradigm shift in how we prove we are who we…
#DataScience #AI #Python
📌 From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read
How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and…
#DataScience #AI #Python
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read
How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and…
#DataScience #AI #Python
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📌 Context Engineering for AI Agents: A Deep Dive
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read
How to optimize context, a precious finite resource for AI agents
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read
How to optimize context, a precious finite resource for AI agents
#DataScience #AI #Python
📌 The Arithmetic of Productivity Boosts: Why Does a “40% Increase in Productivity” Never Actually Work?
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-07 | ⏱️ Read time: 5 min read
Why does grand productivity promises never actually deliver? Is every product just bad, or is…
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-07 | ⏱️ Read time: 5 min read
Why does grand productivity promises never actually deliver? Is every product just bad, or is…
#DataScience #AI #Python
🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning
Both code and weights are available under the MIT license on HuggingFace.
👉 Key details:
• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture
Models:
🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP
⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context
Engineering highlights:
• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality
🌍 Trained on 14 languages (optimized for English and Russian)
Use cases:
• chatbots
• AI assistants
• copilots
• internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
Both code and weights are available under the MIT license on HuggingFace.
👉 Key details:
• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture
Models:
🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP
⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context
Engineering highlights:
• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality
🌍 Trained on 14 languages (optimized for English and Russian)
Use cases:
• chatbots
• AI assistants
• copilots
• internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
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