Python Craft 🐍

Python quiz day #1

Senior Backend Interview: You get only this production timeline. No logs. No source code. No database access. #backend #interview

What will this code output? x = [1, 2, 3] print(x[::-1])

@RahulbeGupta Python 💯

Be honest —As a fresher which language is best for future? 👇

@ayesha_fatiima More optimization like more animation and more formated just results same

ChatGPT on Android - 23MB ChatGPT on iOS - 240MB Are they shipping the whole LLM to iOS? 😭

9 Must - Know Numpy Operations Image Credit: MLTut

@VishalxKodes Chatgpt because it's all rounder

If you could keep only one AI app on your phone, which one would you choose? 👀👇

@_devdeep Sharp mind

She told me I care more about programming than about her. I told her that in the array of my priorities, she's [1]. She was satisfied. 🙂😇

@mathtech789 40

Find X?

@MuhammadAb30483 Thanks

@Python_craft Great 👍

Best python hacks everyone must know these save it

@25Struggler Thank you so much

@Python_craft I just save this image thanks again

@Quotes__Craft Thanks

@Python_craft Very usefull information

@abubakar61swl ❤️

@Python_craft Thank you so much bro usefull information

@Quotes__Craft Wrong bro it's correct is A option

@Python_craft D

Python quiz day #1

@25Struggler Yes

@Python_craft A

@MuhammadAb30483 💯

@Python_craft Most probably A

@abubakar61swl Yes 💯

@Python_craft May be A

@nikks_techie Thanks for explaining

AI/ML gyan: Data Parallelism When training very large AI models, a single GPU may not be enough. There are two common ways to use multiple GPUs: Data Parallelism → Split the data Model Parallelis → Split the model ⸻ Data Parallelism Data Parallelism is a technique where multiple GPUs train the same model, but each GPU processes a different portion of the dataset. Every GPU has an identical copy of the model. ⸻ Example: House Price Prediction Suppose you’re training a house price prediction model using 1 million house records. Instead of sending all 1 million records to one GPU, you divide the dataset into four equal parts. GPU 1 trains on records 1–250,000 GPU 2 trains on records 250,001–500,000 GPU 3 trains on records 500,001–750,000 GPU 4 trains on records 750,001–1,000,000 Each GPU trains the same model independently on its assigned data. After one training step, the GPUs share what they learned (their gradients), combine the updates, and synchronize the model weights. Now every GPU has the same updated model, and training continues. This process repeats until the model is fully trained. ⸻ Real-World Analogy Imagine a teacher has 1,000 exam papers to grade. Instead of one teacher grading everything, four teachers each grade 250 papers using the same marking scheme. After finishing, they combine the results. Everyone followed the same rules—they simply divided the workload. ⸻ One-Line Summary Data Parallelism speeds up training by splitting the dataset across multiple GPUs while keeping an identical copy of the model on every GPU.

@uday_devops > 32GB RAM is the sweet spot. It comfortably handles Cursor, Docker, Chrome, VS Code, and small-to-medium local LLMs at the same time. For larger local models, 64GB+ is even better.

How much RAM is enough for running: • Cursor • Docker • Chrome • VS Code • Local LLMs At the same time?

@factualmann > ChatGPT answers most questions from its training. It only searches the internet when up-to-date or live information is needed. Follow for more.

Interviewer: How can ChatGPT answer questions without searching the internet every single time?