Forwarded from Python Courses & Resources
Free Generative AI Courses
Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
🆓 Free Video Course
⏰ Duration: 30 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructors: Krish Naik, Sunny Savita & Boktiar Ahmed Bappy via freeCodeCamp
🔗 Course Link
5-Day Gen AI Intensive Course with Google
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Free GenAI 65-Hour Bootcamp
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👨🏫 Instructor: Andrew Brown (ExamPro) via freeCodeCamp
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: Multi-hour
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Great Learning Academy
🔗 Course Link
Introduction to Generative AI
🆓 Free Video Course
⏰ Duration: 45 min
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Google Skills
🔗 Course Link
Generative AI for Beginners
📚 Text Course
⏰ Duration: 21 lessons
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Microsoft Cloud Advocates
🔗 Course Link
AI Capabilities and Limitations
🆓 Free Video Course
⏰ Duration: Self-paced
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Anthropic Academy
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: 4 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Simplilearn
🔗 Course Link
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
Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
🆓 Free Video Course
⏰ Duration: 30 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructors: Krish Naik, Sunny Savita & Boktiar Ahmed Bappy via freeCodeCamp
🔗 Course Link
5-Day Gen AI Intensive Course with Google
🆓 Free Video + Hands-On Codelabs
⏰ Duration: 5-day structure
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Created by: Google & Kaggle
🔗 Course Link
Free GenAI 65-Hour Bootcamp
🆓 Free Video Course
⏰ Duration: 65 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructor: Andrew Brown (ExamPro) via freeCodeCamp
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: Multi-hour
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Great Learning Academy
🔗 Course Link
Introduction to Generative AI
🆓 Free Video Course
⏰ Duration: 45 min
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Google Skills
🔗 Course Link
Generative AI for Beginners
📚 Text Course
⏰ Duration: 21 lessons
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Microsoft Cloud Advocates
🔗 Course Link
AI Capabilities and Limitations
🆓 Free Video Course
⏰ Duration: Self-paced
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Anthropic Academy
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: 4 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Simplilearn
🔗 Course Link
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
YouTube
Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
Learn about generative models and different frameworks, investigating the production of text and visual material produced by artificial intelligence. This course was originally recorded live.
Instructors: Krish Naik, Sunny Savita, and Boktiar Ahmed Bappy.…
Instructors: Krish Naik, Sunny Savita, and Boktiar Ahmed Bappy.…
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Build AI products, not scraping infrastructure.
CoreClaw provides ready-to-use Workers & APIs for 1000+ websites — including Google Maps, Instagram, Facebook, YouTube, Amazon, Tiktok and Google Search Scraper.
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✔️ No proxy management
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Your AI helper right in your messenger — in 5 minutes, free
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Amplify (UK) plugs an AI agent straight into your Telegram, WhatsApp, Slack, WeChat, or Discord. Not just a GPT chat — an assistant that reaches into the real world.
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🔖 A useful training tool for Data Scientists 📊
🫡 Real-world tasks from IT companies;
🫡 SQL practice;
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⛓ Link to the training tool
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🫡 Real-world tasks from IT companies;
🫡 SQL practice;
🫡 Python tasks;
🫡 Preparation for Data Science interviews.
⛓ Link to the training tool
https://www.stratascratch.com/
🏷 #DataScience #SQL #Python #InterviewPrep #TechTraining #DataAnalyst
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Forwarded from Machine Learning
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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:
Performance Benchmarks demonstrate speed improvements in various use cases. 📈
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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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
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
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A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. 🚀🤖
https://github.com/visenger/awesome-mlops
#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources
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https://github.com/visenger/awesome-mlops
#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources
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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:
Congrats! You just calculated a U-Net by hand.
💾 Save this post!
#UNet #DeepLearning #AI #NeuralNetworks #ComputerVision #MachineLearning
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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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