🏷 Comprehensive Data Science Roadmap Notes

✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.

🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.

🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.

🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.

🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.

🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.

🌐 #DataScience #DataScience #pytorch #python #Roadmap

https://xn--r1a.website/CodeProgrammer

🔖 50 interview questions for LLM

A good warm-up before the interview: 50 questions on Large Language Models in one document. Not in-depth, but as a checklist to test your knowledge — just perfect.

tags: #LLM #ML #python #pytorch

➡ https://xn--r1a.website/DataAnalyticsX

Assembling GPT-like LLMs from scratch on PyTorch 🔥

https://github.com/analyticalrohit/llms-from-scratch

📚 10 notebooks. Step-by-step explanation.

🧩 Breaks down the architecture of LLMs into simple parts.

✅ Suitable for beginners.

🛠 Completely hands-on.

#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code

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Stop studying LLM from random articles and videos that only explain individual pieces of the puzzle.

📚 LLM from Scratch — this is a practical course on PyTorch for those who want to understand the entire path of modern LLMs: from the first Transformer block to RLHF.

Instead of endless theory, here we gather a complete model training chain:

🔹 Pretraining → Finetuning → Alignment in one course
🔹 Transformer from scratch: positional embeddings, self-attention, multi-head attention, MLP, residual connections, LayerNorm, and full Transformer blocks
🔹 Own training loop without Trainer magic: tokenization, batches, cross-entropy, validation loss, text generation
🔹 Modern architecture improvements: RMSNorm, RoPE, SwiGLU, KV Cache, sliding-window attention, and streaming cache
🔹 Full section on alignment: SFT, reward models, PPO-style RLHF, and GRPO with an analysis of how it looks in the training loop in practice

https://github.com/vivekkalyanarangan30/llm_from_scratch

#LLM #PyTorch #MachineLearning #DeepLearning #AI #Transformer

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

🎓 A Free AI Course for Beginners by Microsoft

For those just getting into artificial intelligence, Microsoft offers a free course.

It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.

The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.

Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.

➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md

What's your AI level right now?

❤️ — Advanced user
🔥 — Almost zero

#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
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🔖 Building our own GPT-like model in PyTorch

We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.

Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.

GitHub: https://github.com/analyticalrohit/llms-from-scratch

#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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

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