Classical machine learning equations and diagrams cheat sheet 📊

https://github.com/soulmachine/machine-learning-cheat-sheet

#MachineLearning #ML #DataScience #CheatSheet #AI #DeepLearning

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Learn AI for free directly from top companies. 🚀

1 - Anthropic:
anthropic.skilljar.com

2 - Google:
grow.google/ai

3 - Meta:
ai.meta.com/resources/

4 - NVIDIA:
developer.nvidia.com/cuda

5 - Microsoft:
learn.microsoft.com/en-us/training/

6 - OpenAI:
academy.openai.com

7 - IBM:
skillsbuild.org

8 - AWS:
skillbuilder.aws

9 - DeepLearning.AI:
deeplearning.ai

10 - Hugging Face:
huggingface.co/learn

💬 Comment "Learning" if you find this helpful.

🔄 Repost so others can take help.

🔖 Must bookmark for future reference.

#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://xn--r1a.website/CodeProgrammer

500 AI/ML/Computer Vision/NLP projects with code 🚀

This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠

All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️

➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience

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Transformers become more understandable when you can "poke" the model directly. 🧠✨

Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. 🔍

It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. 🔄📝

Key features: 🌟

- Live GPT-2 in the browser - experiment without setting up a separate model server 💻
- Your own text - try your own prompts and see how the model processes them ✍️
- Internal components - observe the operations working inside the transformer 🔧
- Focus on predicting the next token - link each visual step to the model's predictions 🎯
- Local development - clone the repository, install dependencies, and run via npm for in-depth study ⚙️

It's open-source (MIT license). 📜

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

#AI #MachineLearning #GPT #DataScience #TechTools #OpenSource

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