Machine Learning

📌 How to Make Claude Code Improve from its Own Mistakes 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read Supercharge Claude Code with continual learning #DataScience #AI #Python

🗂 Cheat sheet on neural networks It clearly presents all the main types of Neural Networks, with a brief theory and useful tips on Python for working with data and machine learning. Essentially, it's a compilation of various cheat sheets in one convenient document. ▶️ Link to the cheat sheet https://www.bigdataheaven.com/wp-content/uploads/2019/02/AI-Neural-Networks.-22.pdf

💾 LLM Architecture Cheat Sheet: from GPT-2 to Trillion-scale Models LLM Architecture Gallery — a page with cards for 39 models (2019–2026): DeepSeek, Qwen, Llama, Kimi, Grok, Nemotron, and others. For each — an architecture diagram, decoder type (dense / sparse MoE / hybrid), attention type, and links to technical reports and configs from HuggingFace. It's clear how the market has converged on MoE + MLA for large models and why hybrid architectures (Mamba-2, DeltaNet, Lightning Attention) are gaining momentum. 🔘 Open Gallery https://sebastianraschka.com/llm-architecture-gallery/ https://t.me/DataScienceT 🔴

📌 Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free) 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-23 | ⏱️ Read time: 24 min read This Article asks what happens next. The model has encoded its knowledge of fraud as… #DataScience #AI #Python

📌 Causal Inference Is Eating Machine Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-23 | ⏱️ Read time: 14 min read Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison… #DataScience #AI #Python

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📌 4 Pandas Concepts That Quietly Break Your Data Pipelines 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-23 | ⏱️ Read time: 10 min read Master data types, index alignment, and defensive Pandas practices to prevent silent bugs in real… #DataScience #AI #Python

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live. https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other. Vision Transformers threw that whole approach out. ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence. Every patch can attend to every other patch from the very first layer. No stacking required. That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬: - Introduction to Vision Transformers and comparison with CNNs - Adapting transformers to images: patch embeddings and flattening - Positional encodings in Vision Transformers - Encoder-only structure for classification - Benefits and drawbacks of ViT - Real-world applications of Vision Transformers - Hands-on: fine-tuning ViT for image classification The Image below shows Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face. The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out. Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps. The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images. 𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤 https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 ViT paper dissection https://youtube.com/watch?v=U_sdodhcBC4 Build ViT from Scratch https://youtube.com/watch?v=ZRo74xnN2SI Original Paper https://arxiv.org/abs/2010.11929 https://t.me/CodeProgrammer

📌 I Built a Podcast Clipping App in One Weekend Using Vibe Coding 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-23 | ⏱️ Read time: 12 min read Rapid prototyping with Replit, AI agents, and minimal manual coding #DataScience #AI #Python

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📌 Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow 🗂 Category: PHYSICS 🕒 Date: 2026-03-22 | ⏱️ Read time: 6 min read A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a… #DataScience #AI #Python

📌 Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-22 | ⏱️ Read time: 9 min read A step-by-step guide to making your OpenAI apps faster, cheaper, and more efficient #DataScience #AI #Python

🐍 PyTorch for Beginners: All the Basics on Tensors in One Place A collection of basic techniques for working with tensors in PyTorch — for those who are starting to get acquainted with the framework and want to quickly master its fundamentals. What's inside:

▶️ What tensors are and why they are needed ▶️ Tensor initialization: zeros, ones, random, similar size ▶️ Type conversion and switching between NumPy and PyTorch ▶️ Arithmetic, logical operations, tensor comparison ▶️ Matrix multiplication and batch computations ▶️ Broadcasting, view(), reshape(), changing dimensions ▶️ Indexing and slicing: how to access parts of a tensor ▶️ Notebook with code examples

A good starting material to understand the mechanics of tensors before moving on to models and training. ⛓ GitHub link tags: #useful ➡ @codeprogrammer

📌 A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-21 | ⏱️ Read time: 21 min read Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP solvers… #DataScience #AI #Python

📌 Escaping the SQL Jungle 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-21 | ⏱️ Read time: 13 min read Most data platforms don’t break overnight; they grow into complexity, query by query. Over time,… #DataScience #AI #Python

📌 The Math That’s Killing Your AI Agent 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-20 | ⏱️ Read time: 12 min read An 85% accurate AI agent fails 4 out of 5 times on a 10-step task.… #DataScience #AI #Python

📌 Agentic RAG Failure Modes: Retrieval Thrash, Tool Storms, and Context Bloat (and How to Spot Them Early) 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-20 | ⏱️ Read time: 8 min read Why agentic RAG systems fail silently in production and how to detect them before your… #DataScience #AI #Python

📌 How to Measure AI Value 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-20 | ⏱️ Read time: 12 min read While efficiency is an important source of AI value, it is only part of the… #DataScience #AI #Python

📌 Building Robust Credit Scoring Models (Part 3) 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-20 | ⏱️ Read time: 18 min read Handling outliers and missing values in borrower data using Python. #DataScience #AI #Python