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graph neural nw ke sath work ke liye

on April 04, 2026

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https://chatgpt.com/share/69d11f52-52cc-8324-af40-0d5e8ab1af3c

ye kaggle ka data

https://www.kaggle.com/competitions/openaimer26-1/data

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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...

df.corr().stack() or unstack()

2nd way import pandas as pd # Example DataFrame data = { "A": [1, 2, 3, 4, 5], "B": [2, 4, 6, 8, 10], "C": [5, 4, 3, 2, 1], "D": [10, 20, 30, 40, 50] } df = pd.DataFrame(data) # Correlation matrix corr = df.corr() # Filter correlations > 0.5 (excluding self-correlation = 1.0) filtered = corr[(corr > 0.5) & (corr < 1.0)] print("Correlation > 0.5:") print(filtered) 🟢 Output Example Correlation > 0.5: A B C D A NaN 1.0 NaN 1.0 B 1.0 NaN NaN 1.0 C NaN NaN NaN NaN D 1.0 1.0 NaN NaN

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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graph neural nw ke sath work ke liye

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