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docker ka use

on May 08, 2026

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watch campusx video

then fastapi wale course m se repo m dockerfile wali file dekh

https://chatgpt.com/share/69fe4e12-148c-83e8-9d78-05c8686a46e8

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Machine learning full roadmap

1. Deep learning specilization deeplearning.ai link- link (do a research what is transfer learning) 2. machine learning ka youtube ka course -> oehle freecodecamp se baad m wscune ka video bhi delh sakta hai usne aur vast topic cover kiya hai link- link machine COMPLETED ON MARCH /1/2025 3.deep learning ka pytorch wala ya tensorflow wala link- deep learning tensorflow part 1 deep learning pytorch 6.ye pleaylist kar le deep learning -: link 5.(deep learning ke liye ye video bhi dekh sakta hai commenet ache the time ile toh dekh liyo-: link ) 6. neural network zero to hero best ( link ) 7. course for tensorflow cnn nlp basic and handon : link 4.machine learning ka zero to hero wali playlist bhi kar sakta hai link - link zero to hero 5. nlp coursera course link - nlp 7. computer vision link - computer vision youtube 6. rienforcement leraning ->sum standford ka ek course bh h...

How to use apply

kaise use karna Bhai .apply() Pandas ka most used aur most powerful function hai — ye tab use hota hai jab: Tum har row ya har column pe koi custom operation lagana chahte ho — especially jab woh operation ek simple loop se slow ho jata. Ab main tujhe .apply() ka use learnable format mein sikhata hoon — taaki logic bhi samjhe , aur yaad bhi rahe. 🧠 Step-by-Step Learnable Format: ✅ 1. .apply() on a column (Series pe) 📌 Use case: Ek column ke sabhi values ka square nikalna import pandas as pd df = pd.DataFrame({ 'number': [1, 2, 3, 4, 5] }) df['square'] = df['number'].apply(lambda x: x**2) print(df) 📤 Output: number square 0 1 1 1 2 4 2 3 9 3 4 16 4 5 25 🧠 Learn this logic: df['col'].apply(function) = apply this function to each value of the column ✅ 2....

important dsa formula

1: PriorityQueue<long[]> occ = new PriorityQueue<>((a, b) -> { if(a[2] != b[2]) { return Long.compare(a[2], b[2]); } else { if(a[0] != b[0]){ return Long.compare(a[0], b[0]); } else { if(a[1] != b[1]){ return Long.compare(a[1], b[1]); } ...

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docker ka use

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