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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers

🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.

📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.

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Stop searching for generative AI resources one category at a time. 🚀

Awesome Generative AI is a curated directory of generative AI projects and services for builders exploring the ecosystem. 🌐

It helps you compare where to look next by organizing links and short descriptions across models, tools, agents, media, and learning resources. 📊

Key features:
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• Multimodal map – explore image, video, audio, and music tools in dedicated sections 🎨🎵
• Learning library – use recommended reading, milestones, courses, guides, and related lists to build context 📚

It’s open-source (CC0-1.0 license). 🔓

Repo: https://github.com/steven2358/awesome-generative-ai

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16 GB RAM. No cloud subscription. Which local AI model actually fits?

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Top YouTube Channels to Master Tech Skills 🚀

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Free Generative AI Courses

Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
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Free GenAI 65-Hour Bootcamp
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Introduction to Generative AI
🆓 Free Video Course
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Generative AI for Beginners
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AI Capabilities and Limitations
🆓 Free Video Course
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Generative AI for Beginners
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Reading Materials

📖 Prompt Engineering Guide
📖 Awesome Generative AI (Curated Resource List)
📖 Generative AI: A Beginner's Guide
📖 Understanding Generative AI Capabilities
📖Stanford HAI: 2025 AI Index Report

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🔖 A useful training tool for Data Scientists 📊

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A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

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Try it, it's free

A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. 🚀🤖

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U-Net by hand ✍️ ~ 17 steps walkthrough below

I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked!

It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL.

Now it is the backbone of diffusion models, which you see in almost all modern image generation models.

I drew the network as a C so the matrix multiplication flows naturally down.

Tilt your head to the right and it is a U again. 🤣

Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself.

= 1. Given =

An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use.

= 2. Convolution 1 =

Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map.

= 3. Find the maxima =

We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down.

= 4. Max pool 1 =

Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good.

= 5. Convolution 2 =

We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now.

= 6. Find the maxima again =

Same move as step 3, on the blue map.

= 7. Max pool 2 =

Eight columns become four.

= 8. The bottleneck =

Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept.

= 9. Spread it out =

We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them.

= 10. Transposed convolution 1 =

Let us fill those gaps by convolving over the spread-out grid. Four columns become eight.

= 11. The first skip =

We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away.

= 12. Convolution with the skip =

Let us convolve the upsampled features together with the copied ones.

= 13. Spread it out again =

Same as step 9, one level up.

= 14. Transposed convolution 2 =

Eight columns become sixteen, back to the width we started at.

= 15. The second skip =

The encoder's first feature map comes across, the one made before any pooling happened.

= 16. Convolution and ReLU =

We convolve, then cross out every negative and set it to zero.

= 17. Output convolution =

Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with.

The outputs:

R' = [3, 0, 7, 0, 7, 0, 17, 0, 3, 0, 9, 0, 2, 0, 6, 0]
G' = [1, 20, 1, 10, 1, 12, 1, 19, 2, 5, 1, 11, 1, 3, 1, 7]
B' = [4, 20, 8, 10, 8, 12, 18, 19, 5, 5, 10, 11, 3, 3, 7, 7]

Congrats! You just calculated a U-Net by hand.

💾 Save this post!

#UNet #DeepLearning #AI #NeuralNetworks #ComputerVision #MachineLearning

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