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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πŸ€–πŸ§  Granite-Speech-3.3-8B: IBM’s Next-Gen Speech-Language Model for Enterprise AI

πŸ—“οΈ 14 Oct 2025
πŸ“š 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
πŸ€–πŸ§  LLaMAX2 by Nanjing University, HKU, CMU & Shanghai AI Lab: A Breakthrough in Translation-Enhanced Reasoning Models

πŸ—“οΈ 14 Oct 2025
πŸ“š 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
πŸ€–πŸ§  Diffusion Transformers with Representation Autoencoders (RAE): The Next Leap in Generative AI

πŸ—“οΈ 14 Oct 2025
πŸ“š 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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πŸ“Œ Using Decision Trees for Exploratory Data Analysis

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-20 | ⏱️ Read time: 7 min read

Add decision trees to your EDA and get great insights from the start
πŸ“Œ Optimizing Sigma Rules in Spark with the Aho-Corasick Algorithm

πŸ—‚ Category: CYBERSECURITY

πŸ•’ Date: 2024-06-20 | ⏱️ Read time: 9 min read

Extending Spark for improved performance in handling multiple search terms
πŸ“Œ Exploratory Data Analysis in 11 Steps

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-19 | ⏱️ Read time: 6 min read

Starting an exploratory data analysis can be daunting. How do you know what to look…
πŸ”— Keras vs. TensorFlow vs. PyTorch: The ultimate showdown for deep learning supremacy! πŸš€

πŸ€” Keras: The user-friendly champion! Perfect for beginners and rapid prototyping.

⚑️ TensorFlow: The powerhouse! Great for complex projects with extensive capabilities.

πŸ”₯ PyTorch: The flexible innovator! With its dynamic computation graph, it’s a favorite among researchers.

πŸ‘‰ @codeprogrammer
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πŸ“Œ How I Dockerized Apache Flink, Kafka, and PostgreSQL for Real-Time Data Streaming

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2024-06-19 | ⏱️ Read time: 12 min read

Integrating pyFlink, Kafka, and PostgreSQL using Docker
πŸ“Œ A Step-By-Step Guide to Building a Programming Language

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2024-06-19 | ⏱️ Read time: 20 min read

Building a programming language from scratch in a few hours
πŸ“Œ Counts Outlier Detector: Interpretable Outlier Detection

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-19 | ⏱️ Read time: 22 min read

An interpretable outlier detector based on multi-dimensional histograms.
πŸ“Œ CLIP, LLaVA, and the Brain

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2024-06-19 | ⏱️ Read time: 10 min read

What neuroscience can teach us about the limitations of modern multimodal transformers
πŸ“Œ 3 Simple Statistical Methods for Outlier Detection

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-18 | ⏱️ Read time: 6 min read

If it works, keep it simple
πŸ“Œ Creating an Assistant with OpenAI Assistant API and Streamlit

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-06-18 | ⏱️ Read time: 7 min read

A step-by-step guide
πŸ“Œ Let’s Revisit Case-When in Different Libraries Including the New Player: Pandas

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-18 | ⏱️ Read time: 6 min read

How to create conditional columns with different tools.
πŸ“Œ AI Agent Capabilities Engineering

πŸ—‚ Category: CHATGPT

πŸ•’ Date: 2024-06-18 | ⏱️ Read time: 13 min read

Introducing a high-level capabilities engineering framework for AI Agents
πŸ“Œ Managing Pivot Table and Excel Charts with VBA

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-18 | ⏱️ Read time: 10 min read

Save precious hours by automating working with pivot tables and charts using VBA
πŸ“Œ Foundation Models in Graph & Geometric Deep Learning

πŸ—‚ Category:

πŸ•’ Date: 2024-06-18 | ⏱️ Read time: 28 min read

In this post, we argue that the era of Graph FMs has already begun and…
πŸ“Œ Learning Triton One Kernel at a Time: Matrix Multiplication

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2025-10-14 | ⏱️ Read time: 14 min read

Tiled GEMM, GPU memory, coalescing, and much more!
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πŸ“Œ Building A Successful Relationship With Stakeholders

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2025-10-14 | ⏱️ Read time: 12 min read

Show your value by moving beyond the technical
πŸ“Œ Why AI Still Can’t Replace Analysts: A Predictive Maintenance Example

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2025-10-14 | ⏱️ Read time: 7 min read

Learn about the limitations of AI in analytics through the example of bearing vibration data…
πŸ“Œ Human Won’t Replace Python

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2025-10-14 | ⏱️ Read time: 23 min read

Why vibe-coding is not a step up from β€œclassic” coding β€” and why it matters