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  ADL : Implementation of Ad-Sherlock for Click-Fraud Detection using Deep-Learning
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@@ -17,47 +18,6 @@ PUP : Phishing URL Prediction using Binary Classification
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  ================================================================================
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- Miniconda : py__3_12_1
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- SHA : ff53a36b7024f8398cbfd043020f1f662cd4c5c2095c0007ddb4348aa5459375
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- --------------------------------------------------------------------------------
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- pip install pipreqs==0.5.0
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- pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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- pip install -r ADL/_/requirements.txt
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- pip install -r APA/_/requirements.txt
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- pip install -r BW__RGB/_/requirements.txt
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- pip install -r FT__BERT_NSP__MRPC/_/requirements.txt
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- pip install -r MNST/_/requirements.txt
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- pip install -r NLP__SQL/_/requirements.txt
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- pip install -r PNSA/_/requirements.txt
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- pip install -r PUP/_/requirements.txt
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- --------------------------------------------------------------------------------
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- pipreqs ADL --savepath ADL/_/requirements.txt --scan-notebooks
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- pipreqs APA --savepath APA/_/requirements.txt --scan-notebooks
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- pipreqs BW__RGB --savepath BW__RGB/_/requirements.txt --scan-notebooks
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- pipreqs FT__BERT_NSP__MRPC --savepath FT__BERT_NSP__MRPC/_/requirements.txt --scan-notebooks
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- pipreqs MNST --savepath MNST/_/requirements.txt --scan-notebooks
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- pipreqs NLP__SQL --savepath NLP__SQL/_/requirements.txt --scan-notebooks
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- pipreqs PNSA --savepath PNSA/_/requirements.txt --scan-notebooks
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- pipreqs PUP --savepath PUP/_/requirements.txt --scan-notebooks
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- <br>
 
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  ADL : Implementation of Ad-Sherlock for Click-Fraud Detection using Deep-Learning
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  ================================================================================
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+ ================================================================================