Question: “Bring the model from GPU to CPU into production ?”, move the model to CPU with ‘m.cpu()’, ‘load_model(m, p)’, back to GPU with ‘m.cuda()’, ‘zip()’ function in PythonĠ0:16:10 Sort the movies, John Travolta Scientology worst movie of all time “Battlefield Earth”, ‘key=itemgetter()jj’, ‘key=lambda’Ġ0:18:30 Embedding interpration, using ‘PCA’ from ‘composition’ for Linear AlgebraĠ0:24:15 Looking at the “Rossmann Retail / Store” Kaggle competition with the ‘Entity Embeddings of Categorical Variables’ paper.Ġ0:41:02 “Rossmann” Data Cleaning / Feature Engineering, using a Test set properly, Create Features (check the Machine Learning “ML1” course for details), ‘apply_cats’ instead of ‘train_cats’, ‘pred_test = m.predict(True)’, result on Kaggle Public Leaderboard vs Private Leaderboard with a poor Validation Set. “Improving the way we work with learning rate”,Ġ0:02:10 Review of last week “Deep Dive into Collaborative Filtering” with MovieLens, analyzing our model, ‘movie bias’, ‘ ‘’, ‘learn.models’, ‘CollabFilterModel’, ‘get_layer_groups(self)’, ‘lesson5-movielens.ipynb’Ġ0:12:10 Jeremy: “I try to use Numpy for everything, except when I need to run it on GPU, or derivatives”, “Implementation of AdamW/SGDW paper in Fastai”, “Optimization for Deep Learning Highlights in 2017” by Sebastian Ruder,
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