AI and Machine Learning
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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
نمایش بیشتر📈 تحلیل کانال تلگرام AI and Machine Learning
کانال AI and Machine Learning (@machine_learning_courses) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 96 300 مشترک است و جایگاه 1 514 را در دسته آموزش و رتبه 2 834 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 96 300 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 25 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 588 و در ۲۴ ساعت گذشته برابر 5 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 7.85% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.53% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 7 561 بازدید دریافت می‌کند. در اولین روز معمولاً 2 437 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 13 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, llm, linkedin, linux, udemy تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 26 اکتبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.
96 300
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🔰 The Data Science Course: Complete Data Science Bootcamp 2026
🌟 4.5 - 161431 votes 💰 Original Price: $59.99
📖 Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning
🔊 Taught By: 365 Careers
📤 Download Full Course 📤 Download All Courses
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Pre-Chunking vs. Post-Chunking (On-Demand Chunking)
This visual breaks down two common ways to chunk documents in Retrieval-Augmented Generation (RAG) systems,and when each makes sense.
Pre-Chunking
Documents are cleaned, split into chunks, embedded, and stored ahead of time.
• Pros: Fast retrieval at query time, simpler runtime pipeline.
• Cons: Rigid,changing chunk size or strategy means reprocessing the entire dataset.
• Best for: Stable datasets, high-throughput apps, predictable queries.
Post-Chunking / On-Demand Chunking
Documents are stored whole; chunking happens after retrieval based on the user’s query.
• Pros: More flexible and query-aware, often more relevant context.
• Cons: Higher latency and infrastructure complexity.
• Best for: Evolving content, exploratory queries, precision-focused use cases.
🔑 Takeaway:
There’s no one-size-fits-all. If speed and scale matter most, pre-chunk. If adaptability and relevance are key, post-chunk. Many production systems even combine both.
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🧠 Memlayer: A Smart Memory Layer for LLM
Memlayer adds intelligent memory to any LLM, enabling agents to remember context and extract structured knowledge. With minimal configuration, it enables fast searching and filtering of important information.
🚀 Key Features:
- Support for universal LLMs (OpenAI, Claude, etc.)
- Smart memory filtering with three modes
- Hybrid search using vector and graph approaches
- High performance (<100 ms) and local data storage
🌐 GitHub: https://github.com/divagr18/memlayer
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Do you want to understand the methods used to train LLMs?
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language 👇
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.
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🔅 The AI-Driven Supply Chain Manager
📝 Learn practical generative AI prompts to help you enhance the efficiency, accuracy, and sustainability of your supply chain.
🌐 Author: Daniel Stanton
🔰 Level: Beginner + Intermediate
⏰ Duration: 41m
📋 Topics: Supply Chain Management, Artificial Intelligence for Business
🔗 Join Artificial intelligence for more courses
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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
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✅ Top Artificial Intelligence Concepts You Should Know 🤖🧠
🔹 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
→ Enables machines to understand and generate human language.
🔹 2. Computer Vision
Use Case: Face recognition, self-driving cars
→ Allows machines to "see" and interpret visual data.
🔹 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
→ AI learns patterns from data to make decisions without explicit programming.
🔹 4. Deep Learning
Use Case: Voice assistants, image recognition
→ A type of ML using neural networks with many layers for complex tasks.
🔹 5. Reinforcement Learning
Use Case: Game AI, robotics
→ AI learns by interacting with the environment and receiving feedback.
🔹 6. Generative AI
Use Case: Text, image, and music generation
→ Models like ChatGPT or DALL·E create human-like content.
🔹 7. Expert Systems
Use Case: Medical diagnosis, legal advice
→ AI systems that mimic decision-making of human experts.
🔹 8. Speech Recognition
Use Case: Voice search, virtual assistants
→ Converts spoken language into text.
🔹 9. AI Ethics
Use Case: Bias detection, fair AI systems
→ Ensures responsible and transparent AI usage.
🔹 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
→ Uses AI to handle rule-based digital tasks efficiently.
💡 Learn these concepts to understand how AI is transforming industries!
💬 Tap ❤️ for more!
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What matters in AI — without the noise.
Elites Zone curates the AI developments worth knowing, with concise analysis and practical context.
Follow Elites Zone for daily AI updates
→ Join here
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❓ Tired of LLMs that hallucinate on complex tasks?
What if your AI could:
• Explore multiple step-by-step reasoning paths?
• Use automated verification to reinforce correct answers?
• Check its own work and self-correct?
• Autonomously decide when to call external tools?
GigaChat 3.5 Reasoning does all of this.
This open-source LLM (built on GigaChat 3.5 Ultra) actually thinks before
answering. It uses proprietary linear attention for efficient long-context
handling, consuming 37% fewer tokens than DeepSeek V4 Flash Preview on math problems.
Real-world performance:
✓ IFBench: 44 → 77
✓ Natural Plan: 64 → 80
✓ LiveCodeBench v6: 56 → 85
🔗 MIT License. Weights on Hugging Face: fp8 | bf16
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Colorizing old black-and-white videos and "bringing faces to life" for FREE
SVFR — a full-fledged framework for restoring faces in videos.
It can:
💬 BFR — improve blurry faces.
💬 Colorization — colorize black-and-white videos.
💬 Inpainting — redraw damaged areas.
💬 and combine all of this in one pass.
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
⚙️ Installation locally:
1. Create an environment
conda create -n svfr python=3.9 -y
conda activate svfrpip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2pip install -r requirements.txtconda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xtpython infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region0 — face enhancement
* 1 — colorization
* 2 — redrawing damage
An ideal tool if:
🟢you're restoring archival videos;
🟢you're creating historical content;
🟢you're working with neural networks and video effects;
🟢you want a wow result without paid services.
▶️ Demo on Hugging Face
🌐 GitHub/Instructions96 294
🔅 Responsible AI Framework for Your Enterprise AI Product
📝 Master responsible AI with the Five Rings framework. Learn how to build ethical, secure, and transparent AI products that align with human values and business success.
🌐 Author: Alina Zhang
🔰 Level: Intermediate
⏰ Duration: 45m
📋 Topics: Responsible AI, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
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Machine Learning in Python (Course Notes)
I just went through an amazing resource on MachineLearning in Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
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🔁 K-Fold Cross Validation
K-Fold exists to answer one honest question:
Will this model work on unseen data?
A single train/test split is unreliable, especially with small datasets.
So K-Fold simulates multiple “future tests” using the same data.
🧠 What It Really Does
Instead of one split, we:
🔀 Divide data into K folds
🔁 Train the model K times
📦 Each time: one fold validates, the rest train
📊 Average the scores
Every sample gets validated once, which reduces evaluation noise and gives a more trustworthy estimate.
Important: It improves evaluation, not the model itself.
⚠️ What People Often Miss
🚫 Do NOT use K-Fold as your final test. Keep a separate test set
⚖️ Use Stratified K-Fold for imbalanced classification.
⏳ Do NOT use standard K-Fold for time series.
📊 K = 5 or 10 is usually enough.
✅ In short K-Fold is just:
A smart way to reuse limited data to simulate multiple real-world tests.
No magic. Just careful evaluation.
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📌 A comprehensive masterclass on Claude Code is available via this repository: https://github.com/luongnv89/claude-howto.
This resource provides a detailed visual and practical guide for one of the most powerful tools for developers. The repository includes:
• Step-by-step learning paths covering basic commands (/init, /plan) to advanced features such as MCP, hooks, and agents, achievable in approximately 11–13 hours. 📚
• An extensive library of custom commands designed for real-world tasks.
• Ready-made memory templates for both individual and team workflows.
• Instructions and scripts for:
- Automated code review.
- Style and standards compliance checks.
- API documentation generation.
• Automation cycles enabling autonomous operation of Claude without direct user intervention. ⚙️
• Integration with external tools, including GitHub and various APIs, presented with step-by-step guidance.
• Diagrams and charts to facilitate understanding, suitable for beginners. 📊
• Examples for configuring highly specialized sub-agents.
• Dedicated learning scripts, such as tools for generating educational books and materials to master specific topics efficiently.
Access the full guide here: https://github.com/luongnv89/claude-howto
