🤖🧠 DeepEyesV2: The Next Leap Toward Agentic Multimodal Intelligence
🗓️ 23 Nov 2025
📚 AI News & Trends
The evolution of artificial intelligence has reached a stage where models are no longer limited to understanding text or images independently. The emergence of multimodal AI systems capable of processing and reasoning across multiple types of data has transformed how machines interpret the world. Yet, most existing multimodal models remain passive observers, unable to act ...
#DeepEyesV2 #AgenticMultimodalIntelligence #MultimodalAI #AIEvolution #ActiveReasoning #AIAction
🗓️ 23 Nov 2025
📚 AI News & Trends
The evolution of artificial intelligence has reached a stage where models are no longer limited to understanding text or images independently. The emergence of multimodal AI systems capable of processing and reasoning across multiple types of data has transformed how machines interpret the world. Yet, most existing multimodal models remain passive observers, unable to act ...
#DeepEyesV2 #AgenticMultimodalIntelligence #MultimodalAI #AIEvolution #ActiveReasoning #AIAction
📌 The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read
During the first days of this Machine Learning Advent Calendar, we explored models based on…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read
During the first days of this Machine Learning Advent Calendar, we explored models based on…
#DataScience #AI #Python
🤖🧠 Reducing Hallucinations in Vision-Language Models: A Step Forward with VisAlign
🗓️ 24 Nov 2025
📚 AI News & Trends
As artificial intelligence continues to evolve, Large Vision-Language Models (LVLMs) have revolutionized how machines understand and describe the world. These models combine visual perception with natural language understanding to perform tasks such as image captioning, visual question answering and multimodal reasoning. Despite their success, a major problem persists – hallucination. This issue occurs when a ...
#VisAlign #ReducingHallucinations #VisionLanguageModels #LVLMs #MultimodalAI #AISafety
🗓️ 24 Nov 2025
📚 AI News & Trends
As artificial intelligence continues to evolve, Large Vision-Language Models (LVLMs) have revolutionized how machines understand and describe the world. These models combine visual perception with natural language understanding to perform tasks such as image captioning, visual question answering and multimodal reasoning. Despite their success, a major problem persists – hallucination. This issue occurs when a ...
#VisAlign #ReducingHallucinations #VisionLanguageModels #LVLMs #MultimodalAI #AISafety
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🤖🧠 LEANN: The Bright Future of Lightweight, Private, and Scalable Vector Databases
🗓️ 24 Nov 2025
📚 AI News & Trends
In the rapidly expanding world of artificial intelligence, data storage and retrieval efficiency have become major bottlenecks for scalable AI systems. The growth of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) has further intensified the demand for fast, private and space-efficient vector databases. Traditional systems like FAISS or Milvus while powerful, are resource-heavy and ...
#LEANN #LightweightVectorDatabases #PrivateAI #ScalableAI #RAG #AIDataStorage
🗓️ 24 Nov 2025
📚 AI News & Trends
In the rapidly expanding world of artificial intelligence, data storage and retrieval efficiency have become major bottlenecks for scalable AI systems. The growth of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) has further intensified the demand for fast, private and space-efficient vector databases. Traditional systems like FAISS or Milvus while powerful, are resource-heavy and ...
#LEANN #LightweightVectorDatabases #PrivateAI #ScalableAI #RAG #AIDataStorage
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🤖🧠 Omnilingual ASR: Meta’s Breakthrough in Multilingual Speech Recognition for 1600+ Languages
🗓️ 24 Nov 2025
📚 AI News & Trends
In an increasingly connected world, speech technology plays a vital role in bridging communication gaps across languages and cultures. Yet, despite rapid progress in Automatic Speech Recognition (ASR), most commercial systems still cater to only a few dozen major languages. Billions of people who speak lesser-known or low-resource languages remain excluded from the benefits of ...
#OmnilingualASR #MultilingualSpeechRecognition #MetaAI #LowResourceLanguages #SpeechTechnology #GlobalCommunication
🗓️ 24 Nov 2025
📚 AI News & Trends
In an increasingly connected world, speech technology plays a vital role in bridging communication gaps across languages and cultures. Yet, despite rapid progress in Automatic Speech Recognition (ASR), most commercial systems still cater to only a few dozen major languages. Billions of people who speak lesser-known or low-resource languages remain excluded from the benefits of ...
#OmnilingualASR #MultilingualSpeechRecognition #MetaAI #LowResourceLanguages #SpeechTechnology #GlobalCommunication
🤖🧠 Whisper by OpenAI: The Revolution in Multilingual Speech Recognition
🗓️ 25 Nov 2025
📚 AI News & Trends
Speech recognition has evolved rapidly over the past decade, transforming the way we interact with technology. From voice assistants to trannoscription services and real-time translation tools, the ability of machines to understand human speech has redefined accessibility, communication and automation. However, one of the major challenges that persisted for years was achieving robust, multilingual and ...
#Whisper #MultilingualSpeechRecognition #OpenAI #SpeechRecognition #AIAccessibility #VoiceTechnology
🗓️ 25 Nov 2025
📚 AI News & Trends
Speech recognition has evolved rapidly over the past decade, transforming the way we interact with technology. From voice assistants to trannoscription services and real-time translation tools, the ability of machines to understand human speech has redefined accessibility, communication and automation. However, one of the major challenges that persisted for years was achieving robust, multilingual and ...
#Whisper #MultilingualSpeechRecognition #OpenAI #SpeechRecognition #AIAccessibility #VoiceTechnology
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📌 How We Are Testing Our Agents in Dev
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-06 | ⏱️ Read time: 5 min read
Testing that your AI agent is performing as expected is not easy. Here are a…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-06 | ⏱️ Read time: 5 min read
Testing that your AI agent is performing as expected is not easy. Here are a…
#DataScience #AI #Python
Generating Fake Data in Python!
Instead of spending time coming up with test data — everything can be generated automatically using the
Installing the library:
Importing and configuring:
Generating basic data:
After running, you will get random values for the name, address, denoscription, email, and country.
Generating multiple records:
🔥 Ideal for test filling of databases. A great way to practice working with external libraries and generating data.
🚪 https://news.1rj.ru/str/DataScienceM
Instead of spending time coming up with test data — everything can be generated automatically using the
Faker library.Installing the library:
pip install faker
Importing and configuring:
from faker import Faker
# Specify the localization
fake = Faker('ru_RU')
Generating basic data:
print(fake.name())
print(fake.address().replace('\n', ', '))
print(fake.text(max_nb_chars=200))
print(fake.email())
print(fake.country())
After running, you will get random values for the name, address, denoscription, email, and country.
Generating multiple records:
for _ in range(5):
print({
"name": fake.name(),
"email": fake.email(),
"address": fake.address().replace('\n', ', '),
"lat": float(fake.latitude()),
"lon": float(fake.longitude()),
"website": fake.url()
})
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Machine Learning
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.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
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📌 How to Climb the Hidden Career Ladder of Data Science
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-07 | ⏱️ Read time: 14 min read
The behaviors that get you promoted
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-07 | ⏱️ Read time: 14 min read
The behaviors that get you promoted
#DataScience #AI #Python
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📌 The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-07 | ⏱️ Read time: 8 min read
In Day 6, we saw how a Decision Tree Regressor finds its optimal split by…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-07 | ⏱️ Read time: 8 min read
In Day 6, we saw how a Decision Tree Regressor finds its optimal split by…
#DataScience #AI #Python
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All my resources will be free and unrestricted there. My goal is to build a clean community exclusively for smart programmers, and I believe Signal is the most suitable platform for this (Signal is the second most popular app after WhatsApp in the US), making it particularly suitable for us as programmers.
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All my resources will be free and unrestricted there. My goal is to build a clean community exclusively for smart programmers, and I believe Signal is the most suitable platform for this (Signal is the second most popular app after WhatsApp in the US), making it particularly suitable for us as programmers.
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Signal Messenger Group
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It’s common to see normalization and standardization used as if they were the same thing, especially because both are often grouped under the generic name “normalization.”
But they have important differences, and choosing the right one can significantly impact model performance.
Even though both techniques are similar, their goal is the same: reduce scale disparities between variables.
For example, a “salary” feature ranging from 10,000 to 1,000,000 can negatively affect certain algorithms.
Distance-based models like K-means and KNN are highly sensitive to scale.
And in algorithms like Linear Regression and Logistic Regression, large differences in variable scale can mislead the model.
That’s why these preprocessing techniques matter so much.
▫️ When to Normalize (MinMaxScaler)
Normalization is useful when:
It makes sense for values to be between 0 and 1, or within a specific interval;
Variables have very different ranges and don’t follow a normal distribution;
You're using algorithms that are sensitive to scale, such as distance-based methods.
▫️ When to Standardize (StandardScaler)
Standardization is ideal when:
The data has no natural bounds and doesn’t need to be between 0 and 1;
You want zero mean and unit variance;
Variables follow (or approximate) a normal distribution;
You use models like Linear Regression, Logistic Regression or PCA.
In short
Standardization: centers the data around mean 0 and std 1, preserving distribution shape.
Normalization: rescales values into a specific interval (usually 0–1), changing the scale without preserving the original distribution.
https://news.1rj.ru/str/DataScienceM
But they have important differences, and choosing the right one can significantly impact model performance.
Even though both techniques are similar, their goal is the same: reduce scale disparities between variables.
For example, a “salary” feature ranging from 10,000 to 1,000,000 can negatively affect certain algorithms.
Distance-based models like K-means and KNN are highly sensitive to scale.
And in algorithms like Linear Regression and Logistic Regression, large differences in variable scale can mislead the model.
That’s why these preprocessing techniques matter so much.
▫️ When to Normalize (MinMaxScaler)
Normalization is useful when:
It makes sense for values to be between 0 and 1, or within a specific interval;
Variables have very different ranges and don’t follow a normal distribution;
You're using algorithms that are sensitive to scale, such as distance-based methods.
▫️ When to Standardize (StandardScaler)
Standardization is ideal when:
The data has no natural bounds and doesn’t need to be between 0 and 1;
You want zero mean and unit variance;
Variables follow (or approximate) a normal distribution;
You use models like Linear Regression, Logistic Regression or PCA.
In short
Standardization: centers the data around mean 0 and std 1, preserving distribution shape.
Normalization: rescales values into a specific interval (usually 0–1), changing the scale without preserving the original distribution.
https://news.1rj.ru/str/DataScienceM
❤4👍1🔥1
📌 Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-07 | ⏱️ Read time: 12 min read
Understanding AI in 2026 — from machine learning to generative models
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-07 | ⏱️ Read time: 12 min read
Understanding AI in 2026 — from machine learning to generative models
#DataScience #AI #Python
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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.
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📌 The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-08 | ⏱️ Read time: 11 min read
Isolation Forest may look technical, but its idea is simple: isolate points using random splits.…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-08 | ⏱️ Read time: 11 min read
Isolation Forest may look technical, but its idea is simple: isolate points using random splits.…
#DataScience #AI #Python
❤3
🤖🧠 Distil-Whisper: Faster, Smaller, and Smarter Speech Recognition by Hugging Face
🗓️ 08 Dec 2025
📚 AI News & Trends
The evolution of Automatic Speech Recognition (ASR) has reshaped how humans interact with technology. From dictation tools and live trannoscription to smart assistants and media captioning, ASR technology continues to bridge the gap between speech and digital communication. However, achieving real-time, high-accuracy trannoscription often comes at the cost of heavy computational requirements until now. Enter ...
#DistilWhisper #FasterSpeechRecognition #SmallerModels #HuggingFace #ASRTechnology #RealTimeTrannoscription
🗓️ 08 Dec 2025
📚 AI News & Trends
The evolution of Automatic Speech Recognition (ASR) has reshaped how humans interact with technology. From dictation tools and live trannoscription to smart assistants and media captioning, ASR technology continues to bridge the gap between speech and digital communication. However, achieving real-time, high-accuracy trannoscription often comes at the cost of heavy computational requirements until now. Enter ...
#DistilWhisper #FasterSpeechRecognition #SmallerModels #HuggingFace #ASRTechnology #RealTimeTrannoscription
📌 The AI Bubble Will Pop — And Why That Doesn’t Matter
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-08 | ⏱️ Read time: 7 min read
How history’s biggest tech bubble explains where AI is headed next
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-08 | ⏱️ Read time: 7 min read
How history’s biggest tech bubble explains where AI is headed next
#DataScience #AI #Python
📌 How to Create an ML-Focused Newsletter
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-08 | ⏱️ Read time: 7 min read
Learn how to make a newsletter with AI tools
#DataScience #AI #Python
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-08 | ⏱️ Read time: 7 min read
Learn how to make a newsletter with AI tools
#DataScience #AI #Python
❤1
📌 Optimizing PyTorch Model Inference on CPU
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-08 | ⏱️ Read time: 20 min read
Flyin’ Like a Lion on Intel Xeon
#DataScience #AI #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-08 | ⏱️ Read time: 20 min read
Flyin’ Like a Lion on Intel Xeon
#DataScience #AI #Python
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📌 Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-09 | ⏱️ Read time: 10 min read
Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-09 | ⏱️ Read time: 10 min read
Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can…
#DataScience #AI #Python
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📌 How to Develop AI-Powered Solutions, Accelerated by AI
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-09 | ⏱️ Read time: 11 min read
From idea to impact : using AI as your accelerating copilot
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-09 | ⏱️ Read time: 11 min read
From idea to impact : using AI as your accelerating copilot
#DataScience #AI #Python
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