Machine Learning
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Real Machine Learning — simple, practical, and built on experience.
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Optimizing the model's performance through Prompt Tuning with the PEFT library.

Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.

📦 First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).

pip install torch transformers peft

The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.

from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
from transformers import AutoModelForCausalLM

peft_config = PromptTuningConfig(
task_type="CAUSAL_LM",
prompt_tuning_init=PromptTuningInit.TEXT,
num_virtual_tokens=20,
prompt_tuning_init_text="Classify the sentiment of this text:",
tokenizer_name_or_path="gpt2"
)

🔄 The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_model = get_peft_model(base_model, peft_config)
peft_model.print_trainable_parameters()

🚀 The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).

python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')"

📝 Expected output: PEFT Setup: OK

pip uninstall peft -y

💡 Prompt Tuning — an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.

#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience

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Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions

💡 Here is your curated list for Data Science interviews!

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#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
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A new collection of free courses has been added:

🔗 https://github.com/dair-ai/ML-Course-Notes

Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚

Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠

What's inside:

• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes

The idea was appreciated. 👍

Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️

#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource

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Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch 🧠

The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄

A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓

Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻

More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/

#Parallax #LLM #AI #DeepLearning #Transformer #TechNews

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If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅

Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖

Instead of endless Google searches, everything is organized into categories:

• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps

Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝

I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️

https://github.com/ZhiningLiu1998/awesome-machine-learning-resources

#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources

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Someone spent several months manually writing a 200-page guide on mathematics and the basics of machine learning. 📘

No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. 🎯

Inside:

• neural networks: backpropagation, SGD, Adam, BatchNorm; ⚙️
• classic ML: SVM, Gradient Boosting, K-Means, PCA; 📊
• hardware for AI: Tensor Cores, Systolic Arrays, CUDA; 🖥️
• transformers: Multi-Head Attention, KV Cache, LoRA; 🧠
• computer vision: ViT, CNN, MAE, IoU, NMS, VLM; 👁️
• agent systems: ReAct, memory, orchestration, OpenClaw. 🤖

The author describes it as the material he would have wanted to receive himself several years ago. 🕰️

And yes, the entire guide is distributed free of charge. 🆓

https://www.arjunvirk.com/writing/ml-guide

#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech

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🎁 SPOTO Mid-Year Sale – Grab Your IT Certification Success Kit!

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🔖 A large collection of AI projects for practice

We found a repository that will help you move from theory to real development of AI applications.

Inside are dozens of ready-made projects: AI analytics, RAG systems, OCR applications, code review agents, travel assistants, and much more.

⛓️ Link to GitHub: https://github.com/Sumanth077/Hands-On-AI-Engineering

#AI #MachineLearning #Python #DataScience #OpenSource #Tech

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10 GitHub repositories that are worth checking out for an AI engineer 🤖

1. Hands-On AI Engineering 🛠️

A collection of AI applications and agent systems with practical use cases of LLM.

👉 https://github.com/Sumanth077/Hands-On-AI-Engineering

2. Hands-On Large Language Models 📘

Full code from the book Hands-On Large Language Models: from basics to fine-tuning.

👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

3. AI Agents for Beginners 🎓

A free course from Microsoft with 11 lessons on creating AI agents.

👉 https://github.com/microsoft/ai-agents-for-beginners

4. GenAI Agents 🤖

A large collection of tutorials and implementations of agent systems.

👉 https://github.com/NirDiamant/GenAI_Agents

5. Made With ML 🚀

About the development, deployment, and support of production-ready ML systems.

👉 https://github.com/GokuMohandas/Made-With-ML

6. Learn Harness Engineering ⚙️

A practical course on Harness Engineering for AI agents.

👉 https://github.com/walkinglabs/learn-harness-engineering

7. AutoResearch 🔬

Autonomous cycles of ML experiments from Andrej Karpathy.

👉 https://github.com/karpathy/autoresearch

8. Designing Machine Learning Systems 📚

Notes and materials from Chip Huyen's book.

👉 https://github.com/chiphuyen/dmls-book

9. Awesome LLM Inference

A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.

👉 https://github.com/xlite-dev/Awesome-LLM-Inference

10. LLM Course 🗺️

A practical course on LLM with a roadmap and Colab notebooks.

👉 https://github.com/mlabonne/llm-course

#AI #MachineLearning #LLM #DataScience #Tech #GitHub

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