AI and Machine Learning: Difference between revisions

AI and Machine Learning (view source)

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Diane27

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 ... you should consider grouping many jobs into one. [[META: A package for job farming|META]], [[GLOST]], and [[GNU Parallel]] are available to help you with this.
-== Experiment Tracking and Hyperparameter Optimization == <!--T:27-->
+== Experiment tracking and hyperparameter optimization == <!--T:27-->
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 Note that Comet and Wandb are not currently available on Graham.
-== Large Scale Machine Learning (Big Data) == <!--T:40-->
+== Large-scale machine learning (big data) == <!--T:40-->
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-Modern Deep Learning packages like pytorch and tensorflow include utilities to handle large scale training natively and tutorials on how to do it abound. Scaling classic Machine Learning (i.e., not Deep Learning) methods, however, is not as widely discussed and can often be a frustrating problem to solve. [[Large_Scale_Machine_Learning_(Big_Data)|This guide]] contains ideas and practical options, along with tutorials, to tackle training classic ML models on very large datasets.
+Modern deep learning packages like Pytorch and TensorFlow include utilities to handle large-scale training natively and tutorials on how to do it abound. Scaling classic machine learning (i.e., not deep learning) methods, however, is not as widely discussed and can often be a frustrating problem to solve. [[Large_Scale_Machine_Learning_(Big_Data)|This guide]] contains ideas and practical options, along with tutorials, to tackle training classic ML models on very large datasets.
 == Troubleshooting == <!--T:31-->