📌 Build and Deploy Your First Supply Chain App in 20 Minutes
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-04 | ⏱️ Read time: 21 min read
A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated…
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-04 | ⏱️ Read time: 21 min read
A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated…
#DataScience #AI #Python
📌 Bootstrap a Data Lakehouse in an Afternoon
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-04 | ⏱️ Read time: 12 min read
Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB
#DataScience #AI #Python
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-04 | ⏱️ Read time: 12 min read
Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB
#DataScience #AI #Python
📌 The Best Data Scientists are Always Learning
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read
Why continuous learning matters & how to come up with topics to study
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read
Why continuous learning matters & how to come up with topics to study
#DataScience #AI #Python
📌 Reading Research Papers in the Age of LLMs
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read
How I keep up with papers with a mix of manual and AI-assisted reading
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read
How I keep up with papers with a mix of manual and AI-assisted reading
#DataScience #AI #Python
🤖🧠 Supervised Reinforcement Learning: A New Era of Step-Wise Reasoning in AI
🗓️ 23 Nov 2025
📚 AI News & Trends
In the evolving landscape of artificial intelligence, large language models (LLMs) like GPT, Claude and Qwen have demonstrated remarkable abilities from generating human-like text to solving complex problems in mathematics, coding, and logic. Yet, despite their success, these models often struggle with multi-step reasoning, especially when each step depends critically on the previous one. Traditional ...
#SupervisedReinforcementLearning #StepWiseReasoning #ArtificialIntelligence #LargeLanguageModels #MultiStepReasoning #AIBreakthrough
🗓️ 23 Nov 2025
📚 AI News & Trends
In the evolving landscape of artificial intelligence, large language models (LLMs) like GPT, Claude and Qwen have demonstrated remarkable abilities from generating human-like text to solving complex problems in mathematics, coding, and logic. Yet, despite their success, these models often struggle with multi-step reasoning, especially when each step depends critically on the previous one. Traditional ...
#SupervisedReinforcementLearning #StepWiseReasoning #ArtificialIntelligence #LargeLanguageModels #MultiStepReasoning #AIBreakthrough
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🤖🧠 CALM: Revolutionizing Large Language Models with Continuous Autoregressive Learning
🗓️ 23 Nov 2025
📚 AI News & Trends
Large Language Models (LLMs) such as GPT, Claude and Gemini have dramatically transformed artificial intelligence. From generating natural text to assisting in code and research, these models rely on one fundamental process: autoregressive generation predicting text one token at a time. However, this sequential nature poses a critical efficiency bottleneck. Generating text token by token ...
#CALM #ContinuousAutoregressiveLearning #LargeLanguageModels #AutoregressiveGeneration #AIEfficiency #AIInnovation
🗓️ 23 Nov 2025
📚 AI News & Trends
Large Language Models (LLMs) such as GPT, Claude and Gemini have dramatically transformed artificial intelligence. From generating natural text to assisting in code and research, these models rely on one fundamental process: autoregressive generation predicting text one token at a time. However, this sequential nature poses a critical efficiency bottleneck. Generating text token by token ...
#CALM #ContinuousAutoregressiveLearning #LargeLanguageModels #AutoregressiveGeneration #AIEfficiency #AIInnovation
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🤖🧠 Agent-o-rama: The End-to-End Platform Transforming LLM Agent Development
🗓️ 23 Nov 2025
📚 AI News & Trends
As large language models (LLMs) become more capable, developers are increasingly using them to build intelligent AI agents that can perform reasoning, automation and decision-making tasks. However, building and managing these agents at scale is far from simple. Challenges such as monitoring model behavior, debugging reasoning paths, testing reliability and tracking performance metrics can make ...
#AgentoRama #LLMAgents #EndToEndPlatform #AIIntelligence #ModelMonitoring #AIDevelopment
🗓️ 23 Nov 2025
📚 AI News & Trends
As large language models (LLMs) become more capable, developers are increasingly using them to build intelligent AI agents that can perform reasoning, automation and decision-making tasks. However, building and managing these agents at scale is far from simple. Challenges such as monitoring model behavior, debugging reasoning paths, testing reliability and tracking performance metrics can make ...
#AgentoRama #LLMAgents #EndToEndPlatform #AIIntelligence #ModelMonitoring #AIDevelopment
Forwarded from Machine Learning with Python
Our Group on Signal (only for Programmers)
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
🤖🧠 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 transcription 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 transcription 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
❤1
📌 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, description, 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://t.me/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, description, 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
❤1
I'm pleased to invite you to join my private Signal group.
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.
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
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.
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
signal.group
Signal Messenger Group
Follow this link to join a group on Signal Messenger.
❤1
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://t.me/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://t.me/DataScienceM
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