Machine Learning – Telegram
Machine Learning
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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.

Admin: @HusseinSheikho || @Hussein_Sheikho
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📌 Challenges and Solutions in Data Mesh – Part 3

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-06-21 | ⏱️ Read time: 12 min read

A practical approach to achieving interoperability in the data mesh through federated enterprise data modeling
📌 Beyond Kleinberg’s Impossibility Theorem of Clustering: A Pragmatic Clustering Evaluation Framework

🗂 Category:

🕒 Date: 2024-06-21 | ⏱️ Read time: 15 min read

This article explores a pragmatic evaluation framework for clustering under the constraint of Kleinberg’s Impossibility…
📌 Deep Learning Illustrated, Part 5: Long Short-Term Memory (LSTM)

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-06-21 | ⏱️ Read time: 8 min read

An illustrated and intuitive guide on the inner workings of an LSTM
📌 Entity-Resolved Knowledge Graphs

🗂 Category: DATA SCIENCE

🕒 Date: 2024-06-21 | ⏱️ Read time: 5 min read

New words. Old concepts. In the end, it’s about data fusion.
📌 Creating a Streamlit App for Satellite Imagery Visualization: A Step-by-Step Guide

🗂 Category: DATA VISUALIZATION

🕒 Date: 2024-06-21 | ⏱️ Read time: 11 min read

Explore any point on Earth at any time using satellite data with Streamlit
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📌 Simplifying Support Vector Machines – A Concise Introduction into Binary Classification

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-06-21 | ⏱️ Read time: 7 min read

MLBasics #4: The Binary Classification King – A Journey Through Support Vector Machines
📌 Guiding an LLM’s Response to Create Structured Output

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-06-21 | ⏱️ Read time: 11 min read

Learn how to structure a language model’s response to ensure that the response format is…
📌 Enhancing Marketing Mix Modelling with Causal AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-06-21 | ⏱️ Read time: 8 min read

Causal AI, exploring the integration of causal reasoning into machine learning
📌 3 Painful Mistakes I Made as a Junior Data Scientist

🗂 Category: CAREER ADVICE

🕒 Date: 2024-06-21 | ⏱️ Read time: 6 min read

Learn from them to fast-track your career today
📌 Voyage Multilingual 2 Embedding Evaluation

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-06-20 | ⏱️ Read time: 11 min read

Compared to OpenAI, Cohere, Google, and E5
📌 Transforming Next-Token Prediction into Classification with LLMs

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-06-20 | ⏱️ Read time: 7 min read

From tokens to labels: Performing classification with large language models
📌 Understanding Techniques for Solving GenAI Challenges

🗂 Category:

🕒 Date: 2024-06-20 | ⏱️ Read time: 19 min read

Dive into model pre-training, fine-tuning, RAG, prompt engineering, and more!
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📌 How I Created a Kaggle-Like Platform for My Students Using Streamlit and How You Can Do It as Well

🗂 Category: DATA SCIENCE

🕒 Date: 2024-06-20 | ⏱️ Read time: 29 min read

Gamify machine learning student projects with Streamlit and Google Sheets
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📌 How Do Computers Actually Remember?

🗂 Category: DATA SCIENCE

🕒 Date: 2024-06-20 | ⏱️ Read time: 15 min read

A Budding Data Scientist’s Introduction to Computer Hardware
📌 Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting

🗂 Category: DEEP LEARNING

🕒 Date: 2024-06-20 | ⏱️ Read time: 14 min read

What you need instead: Learnings from the Makridakis M5 competitions and the 2023 Kaggle AI…
📌 Should You Join FAANG or a Startup as a Data Scientist?

🗂 Category: CAREER ADVICE

🕒 Date: 2024-06-20 | ⏱️ Read time: 11 min read

Lessons from working at Uber + Meta, a growth stage company and a tiny startup
📌 Back to Basics: Databases, SQL, and Other Data-Processing Must-Reads

🗂 Category: DATA SCIENCE

🕒 Date: 2024-06-20 | ⏱️ Read time: 3 min read

Our weekly selection of must-read Editors’ Picks and original features
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🤖🧠 Thinking with Camera 2.0: A Powerful Multimodal Model for Camera-Centric Understanding and Generation

🗓️ 14 Oct 2025
📚 AI News & Trends

In the rapidly evolving field of multimodal AI, bridging gaps between vision, language and geometry is one of the frontier challenges. Traditional vision-language models excel at describing what is in an image “a cat on a sofa” “a red car on the road” but struggle to reason about how the image was captured: the camera’s ...

#MultimodalAI #CameraCentricUnderstanding #VisionLanguageModels #AIResearch #ComputerVision #GenerativeModels
🤖🧠 Granite-Speech-3.3-8B: IBM’s Next-Gen Speech-Language Model for Enterprise AI

🗓️ 14 Oct 2025
📚 AI News & Trends

In the fast-growing field of speech and language AI, IBM continues to make strides with its Granite model family , a suite of open enterprise-grade AI models that combine accuracy, safety and efficiency. The latest addition to this ecosystem, Granite-Speech-3.3-8B marks a significant milestone in automatic speech recognition (ASR) and speech translation (AST) technology. Released ...

#SpeechAI #LanguageModel #EnterpriseAI #ASR #SpeechTranslation #GraniteModel
🤖🧠 LLaMAX2 by Nanjing University, HKU, CMU & Shanghai AI Lab: A Breakthrough in Translation-Enhanced Reasoning Models

🗓️ 14 Oct 2025
📚 AI News & Trends

The world of large language models (LLMs) has evolved rapidly, producing advanced systems capable of reasoning, problem-solving, and creative text generation. However, a persistent challenge has been balancing translation quality with reasoning ability. Most translation-enhanced models excel in linguistic diversity but falter in logical reasoning or coding tasks. Addressing this crucial gap, the research paper ...

#LLaMAX2 #TranslationEnhanced #ReasoningModels #LargeLanguageModels #NanjingUniversity #HKU
🤖🧠 Diffusion Transformers with Representation Autoencoders (RAE): The Next Leap in Generative AI

🗓️ 14 Oct 2025
📚 AI News & Trends

Diffusion Transformers (DiTs) have revolutionized image and video generation enabling stunningly realistic outputs in systems like Stable Diffusion and Imagen. However, despite innovations in transformer architectures and training methods, one crucial element of the diffusion pipeline has remained largely stagnant- the autoencoder that defines the latent space. Most current diffusion models still depend on Variational ...

#DiffusionTransformers #RAE #GenerativeAI #StableDiffusion #Imagen #LatentSpace
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