Machine Learning

📌 Building Human-In-The-Loop Agentic Workflows

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-25 | ⏱️ Read time: 10 min read

Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph

#DataScience #AI #Python

📌 The Machine Learning Lessons I’ve Learned This Month

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-03-25 | ⏱️ Read time: 5 min read

Proactivity, blocking, and planning

#DataScience #AI #Python

📌 Following Up on Like-for-Like for Stores: Handling PY

🗂 Category: DATA ANALYSIS

🕒 Date: 2026-03-25 | ⏱️ Read time: 7 min read

My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with…

#DataScience #AI #Python

📌 The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read

How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…

#DataScience #AI #Python

📌 Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read

We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…

#DataScience #AI #Python

📌 From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read

How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making

#DataScience #AI #Python

Visualizing the complexity of algorithms

https://t.me/CodeProgrammer ✅

📌 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

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

📌 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

🎁 23 Years of SPOTO – Claim Your Free IT Certs Prep Kit!

🔥Whether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!

✅ Free Resources :
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4lk4m3c
・IT Certs E-book: https://bit.ly/4bdZOqt
・IT Exams Skill Test: https://bit.ly/4sDvi0b
・Free AI material and support tools: https://bit.ly/46TpsQ8
・Free Cloud Study Guide: https://bit.ly/4lk3dIS


👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/Cnc5M5353oSBo3savBl397

💬 Want exam help? Chat with an admin now!
wa.link/rozuuw