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- Oct 29, 2024
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Vikas Sharma authored
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Jonas Klesen authored
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Vikas Sharma authored
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- Oct 28, 2024
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Vikas Sharma authored
Cell no. 3, showing error
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terrchen authored
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- Oct 27, 2024
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himanshushukla12 authored
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himanshushukla12 authored
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himanshushukla12 authored
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Himanshu Shukla authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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- Oct 26, 2024
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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- Oct 25, 2024
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
Co-authored-by:
Hamid Shojanazeri <hamid.nazeri2010@gmail.com>
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Sanyam Bhutani authored
Co-authored-by:
Hamid Shojanazeri <hamid.nazeri2010@gmail.com>
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Sanyam Bhutani authored
Co-authored-by:
Hamid Shojanazeri <hamid.nazeri2010@gmail.com>
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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- Oct 24, 2024
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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celestinoalan authored
**Problem** Currently, we're val_loss.append(best_val_loss) in each epoch. This is misleading because we're appending the corresponding epoch (not best across epochs) quantities in train_loss, train_prep, and val_prep. This is also inconvenient, as one often would like to plot both train and validation losses as a function of the epochs to look for overfitting. **Solution** val_loss.append(eval_epoch_loss)
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Sanyam Bhutani authored
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- Oct 23, 2024
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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Sanyam Bhutani authored
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