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
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
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Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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📱 TorchCode — a PyTorch training tool for preparing for ML interviews
40 tasks for implementing operators and architectures that are actually asked in interviews. Automatic checking, hints, and reference solutions — all in the browser without installation.
If you're preparing for an ML interview, it's useful to go through at least half of them.
Link: https://github.com/duoan/TorchCode
tags: #useful #pytorch
https://t.iss.one/CodeProgrammer✅
40 tasks for implementing operators and architectures that are actually asked in interviews. Automatic checking, hints, and reference solutions — all in the browser without installation.
If you're preparing for an ML interview, it's useful to go through at least half of them.
Link: https://github.com/duoan/TorchCode
tags: #useful #pytorch
https://t.iss.one/CodeProgrammer
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✏️ SQL for Data Science in 6 Weeks
An experienced data scientist has put together a step-by-step plan for learning SQL, organizing it into a repository on GitHub. Each week — a new topic with useful materials and practice.
The program looks like this:
▶️ Week 1 — SQL basics and data extraction;
▶️ Week 2 — grouping using GROUP BY;
▶️ Week 3 — all types of JOIN and where to apply them;
▶️ Week 4 — analyzing window functions;
▶️ Week 5 — CTE and subqueries;
▶️ Week 6 — final project to reinforce.
A great start for those who want to improve their SQL skills specifically for analytics and Data Science tasks.
➡️ Link to the course
https://github.com/andresvourakis/free-6-week-sql-roadmap-data-science
https://t.iss.one/DataAnalyticsX🟡
An experienced data scientist has put together a step-by-step plan for learning SQL, organizing it into a repository on GitHub. Each week — a new topic with useful materials and practice.
The program looks like this:
▶️ Week 1 — SQL basics and data extraction;
▶️ Week 2 — grouping using GROUP BY;
▶️ Week 3 — all types of JOIN and where to apply them;
▶️ Week 4 — analyzing window functions;
▶️ Week 5 — CTE and subqueries;
▶️ Week 6 — final project to reinforce.
A great start for those who want to improve their SQL skills specifically for analytics and Data Science tasks.
https://github.com/andresvourakis/free-6-week-sql-roadmap-data-science
https://t.iss.one/DataAnalyticsX
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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
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
✅ https://t.iss.one/Codeprogrammer
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Forwarded from Udemy Free Coupons
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NumPy | SciPy | Matplotlib | Pandas | Machine Learning | Data Science | Deep Learning | Pre-Machine Learning Analysis...
🏷 Category: development
🌍 Language: English (US)
👥 Students: 51,515 students
⭐️ Rating: 4.2/5.0 (543 reviews)
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🌍 Language: English (US)
👥 Students: 19,230 students
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💎 By: https://t.iss.one/DataScienceC
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SQL Basics.pdf
102.8 KB
💻 Collection of cheat sheets on SQL
I've gathered for you short and understandable cheat sheets on the main topics:
▶️ Basics of the SQL language;
▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.
An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.
tags: #sql #useful
https://t.iss.one/DataAnalyticsX
I've gathered for you short and understandable cheat sheets on the main topics:
▶️ Basics of the SQL language;
▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.
An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.
tags: #sql #useful
https://t.iss.one/DataAnalyticsX
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Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Machine Learning with Python
The most complete list of video courses on Computer Science on the internet.
cs-video-courses — 78K+ stars.
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From beginner level (CS50) to advanced (6.824 Distributed Systems).
The curriculum is free.🤙
https://github.com/Developer-Y/cs-video-courses
https://t.iss.one/CodeProgrammer⚡️
Save & Share & Like🏃♀️
cs-video-courses — 78K+ stars.
MIT.
Stanford University.
University of California, Berkeley.
Harvard University.
Carnegie Mellon University.
Indian Institutes of Technology.
Princeton University.
California Institute of Technology.
Everything is free. All lectures are in video format. Everything is collected in one repository.
Topics:
→ Data structures and algorithms
→ Operating systems
→ Distributed systems
→ Database systems
→ Computer networks
→ Machine learning
→ Deep learning
→ Natural language processing (NLP)
→ Computer vision
→ Computer graphics
→ Security
→ Quantum computing
→ Robotics
→ Blockchain
From beginner level (CS50) to advanced (6.824 Distributed Systems).
The curriculum is free.
https://github.com/Developer-Y/cs-video-courses
https://t.iss.one/CodeProgrammer
Save & Share & Like
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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
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
✅ https://t.iss.one/Codeprogrammer
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Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🚀 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.
❤2
Forwarded from Machine Learning with Python
✔️ 10 Books to Understand How Large Language Models Function (2026)
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖
❤2
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
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
✅ https://t.iss.one/Codeprogrammer
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