Have you seen the interactive explanation of the Transformer?

It's a really cool visualization of how the architecture works.

https://poloclub.github.io/transformer-explainer/

One of the best resources for SQL performance:

use-the-index-luke.com

https://t.iss.one/DataAnalyticsX

Exporting the model from PyTorch to the universal ONNX format for independent inference 🚀

Deploying PyTorch models in production often requires installing a large framework and relying on a Python environment. The ONNX (Open Neural Network Exchange) format converts the computation graph into an intermediate binary format suitable for running on any device and programming language. We will export the PyTorch neural network and run its inference using the lightweight ONNX Runtime. 🛠

To export and run the neural network, we will install the PyTorch framework, the ONNX library, and the cross-platform ONNX Runtime engine.

pip install torch onnx onnxruntime

The packages for converting and high-performance execution of graphs have been successfully installed. ✅

We will write a Python script that creates a test PyTorch model, exports it to a .onnx file, and immediately performs a verification of the output.

import torch, torch.nn as nn, onnxruntime as ort, numpy as np

model = nn.Sequential(nn.Linear(10, 5), nn.ReLU())
x = torch.randn(1, 10)
torch.onnx.export(model, x, "model.onnx", input_names=["input"], output_names=["output"])

session = ort.InferenceSession("model.onnx")
res = session.run(None, {"input": x.numpy()})
print("ONNX Output shape:", res[0].shape)

The model graph has been successfully serialized into a binary file, and the runtime performed the prediction without involving PyTorch. 📦

# verification (checks the correctness and structure of the saved ONNX model)

python3 -c "import onnx; model = onnx.load('model.onnx'); onnx.checker.check_model(model); print('ONNX Model Status: Valid')"

Expected output: ONNX Model Status: Valid

# cleanup (deletes the generated model file and cleans up binaries)

rm -f model.onnx

Converting neural networks to ONNX allows you to decouple inference from Python and run models in C++, Rust, Go, or directly in a web browser. Be sure to specify the names of the input and output tensors when exporting to simplify integration with the service. 💻

#PyTorch #ONNX #MachineLearning #DeepLearning #AI #DevOps

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Hands On Python Data Science - Data Science Bootcamp

Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning

🏷 Category: Development
🌍 Language: English
👥 Students: 31,524 students
⭐️ Rating: 4.3/5.0
💰 Price: $14.99 ⟹ FREE
🆔 Coupon: •••••••••• (tap below to reveal)

🔓 Tap "Get Coupon" below — the code unlocks inside the app after a short rewarded ad.

💎 By: https://t.iss.one/Udemy26
#Programming #Coding #Development #Tech #Python #DataScience

100 Machine Learning Interview Questions and Answers 🤖

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

A distinctive bot for subscribing to courses on the most popular educational platforms across multiple fields, most notably programming, artificial intelligence, mechanics, medicine, and other sciences. 🎓🤖

This bot provides a free subscription to any course available within the bot. 🆓

https://t.iss.one/UdemySybot?start=ref_418788114

#FreeCourses #Programming #AI #Education #Udemy #TelegramBots

One of the key moments when I truly understood how transformers work: 🧠✨

"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."

"Start thinking of it as a residual flow." 🌊

Each block in a transformer has a residual connection that adds the input of the block to its output:

x' = f(x) + x

Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕

Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.

Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗

I owe these ideas to an older article by Anthropic about the architecture of transformers:
transformer-circuits.pub/2021/framework

#Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 Over 300 tools for LLM evaluation in one place.

This repository contains a comprehensive catalog of solutions for evaluating LLMs, RAG systems, agents, observability, benchmarks, and guardrails.

Save this to avoid searching for the tools you need across GitHub.
https://github.com/aglio-lab/ai-evaluation-tools

#LLMEvaluation #AIBenchmarks #RAG #AIAgents #GitHub #AITools

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

You can literally enter a sentence and see how the Transformer processes it.

This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process:

Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token.

You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation.

If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this.

https://poloclub.github.io/transformer-explainer/

https://t.iss.one/DataAnalyticsX

https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8

Follow me to more posts and useful content

If you want to get GOAT-level at software engineering,

save & star these 10 GitHub repos:

1 System design academy

↳ https://github.com/systemdesign42/system-design-academy

2 Public APIs

↳ https://github.com/public-apis/public-apis

3 Tech interview handbook

↳ https://github.com/yangshun/tech-interview-handbook

4 Coding interview university

↳ https://github.com/jwasham/coding-interview-university

5 Engineering leadership

↳ https://github.com/gregorojstersek/resources-to-become-a-great-engineering-leader

6 Freecodecamp

↳ https://github.com/freeCodeCamp/freeCodeCamp

7 Developer roadmaps

↳ https://github.com/kamranahmedse/developer-roadmap

8 Path to senior engineer handbook

↳ https://github.com/jordan-cutler/path-to-senior-engineer-handbook

9 Free programming books

↳ https://github.com/EbookFoundation/free-programming-books

10 Build your own x

↳ https://github.com/codecrafters-io/build-your-own-x

Anything else you'd add?

♻ Repost to help others become better engineers.
https://t.iss.one/DataAnalyticsX 🧑🏻‍💻

🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup

If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.

📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers
→ https://bit.ly/4yrk1Ti

🪜 Online Free Course — structured path, works as a refresher before exams
→ https://bit.ly/4hUprRP

☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks
→ https://bit.ly/4yt1iXy

📊 Cloud Study Guide — cheat-sheet style, handy for interview prep
→ https://bit.ly/4el4Eod

🧠 Free Mock Exam — test yourself before booking the real thing
→ https://bit.ly/4sbWlkl

📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪

🌐 Community: https://chat.whatsapp.com/CS7VOvQCLZG5BJPazBGm1v
📲 Personalized help: https://wa.link/xd2fu6
#Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers

I thought I understood how GPU loading works until I started writing this guide. 🤔

I was constantly confusing three different concepts:

• The amount of work we are launching.
• The amount of work that is actually placed on the GPU.
• The amount of work that is ready to be executed right now.

But these are completely different things. ⚠️

Consider a CUDA kernel with 240 blocks, each containing 256 threads. This results in 61,440 logical threads, or 1920 warps. However, the number of launched threads doesn't tell you how many warps can actually be present on the GPU simultaneously. 🧮

Blocks are placed on streaming multiprocessors (SMs) based on the available registers, shared memory, and limitations on the number of threads, warps, and blocks.

Even after being placed on an SM, a warp can be waiting for a memory operation to complete, a dependency between arithmetic operations to be resolved, or for synchronization. The scheduler can only select warps that are actually ready to issue instructions for execution. ⏳

This changed my approach to analyzing performance metrics. 📊

Occupancy is a measure of the ratio of the number of active warps on an SM to the maximum number of warps that the SM can support.

GPU utilization measures something different. In NVML, this metric reflects the percentage of the measurement interval during which at least one CUDA kernel was running.

Therefore, the GPU can show 100% utilization even if the Tensor Cores are not being used to their full potential, the HBM bandwidth is not saturated, and performance is limited by one of the system components. At the same time, a significant portion of the GPU's resources may remain underutilized. 📉

Similar misconceptions arise with memory. Registers, shared memory, L1, L2, and HBM are not simply levels of memory that differ in speed. Each has its own areas of accessibility, capacity, management characteristics, and access methods. 💾

If a model fits in HBM, it only means that there is enough video memory for it. But it tells you very little about how many bytes need to be transferred, how efficiently memory accesses are combined, how often data is reused, and whether memory is actually limiting the performance of the CUDA kernel.

A good example is FlashAttention. This algorithm calculates the exact attention mechanism, but uses tiling and repeated computations to reduce the amount of data transferred between HBM and the GPU's internal memory. ⚡️

Tensor Cores turned out to be another technology that I previously understood too simplistically.

These are specialized computational units for matrix multiplication operations with accumulation, which play a huge role in AI tasks. However, they do not "execute the model" as a whole. 🧠

Reduction operations, indexing, element-wise calculations, synchronization, and data movement still involve other components of the GPU. In addition, launching CUDA kernels also introduces overhead.

Over time, I stopped asking myself: "Why isn't the GPU 100% utilized?" Instead, I started asking a more accurate question: "What is currently limiting the speed of performing useful work?" 🤔

Ultimately, all of this turned into a 42-page guide. 📄

It covers streaming multiprocessors (SMs), warps, and schedulers, the memory hierarchy and latency hiding, Tensor Cores, the distribution of GEMM computations across GPU hardware resources, numerical precision, and performance metrics that are very easy to misinterpret.

https://drive.google.com/file/d/17tyvxr__8vZ6o995GNq6I5PC5JOiv0ql/view

#GPU #CUDA #AI #DeepLearning #PerformanceOptimization #TechGuide

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A