🔖 A huge open-source course on AI Engineering from scratch

In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.

Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀

⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch

#AI #MachineLearning #Python #Rust #OpenSource #Tech

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Transformer implementations for vision, audio, and AI agents 🤖👁️🎵

Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide

#AI #MachineLearning #Vision #Audio #Agents #Tech

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Introduction to Deep RL and DQN

Link: https://www.dailydoseofds.com/rl-course-part-6/

🤖 #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #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.
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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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🚀 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
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Data Science Interview Questions

💡 Here is your curated list for Data Science interviews!

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#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep

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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🚀 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.
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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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🚀 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.
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