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The optimization algorithm is an extension ofa coordinate descent methodfollowing a similar path proposed in an earlierpaper. It is usually much faster thanL-BFGSandtruncated Newton methodsfor large-scale and sparse data sets. This class usesempirical risk minimization(i.e., ERM) to formulate the...
It is usually much faster than L-BFGS and truncated Newton methods for large-scale and sparse data sets.This class uses empirical risk minimization (i.e., ERM) to formulate the optimization problem built upon collected data. Note that empirical risk is usually measured by applying a loss ...
Split the dataset into a training and testing set. Apply PCA to the training dataset. Get the score using the testing dataset. The score represents the average log-likelihood of all samples. from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler X =...
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ReFT enables intervention-based model training and serving at scale. It allows continuous batching while only keeping a single copy of the base LM. The base LM, when intervened, can solve different user tasks with batched inputs. Our toy example above shows the minimum setup for training with...
After watching all the videos of the famous Standford's CS231n course that took place in 2017, i decided to take summary of the whole course to help me to remember and to anyone who would like to know about it. I've skipped some contents in some lectures as it wasn't important to ...
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