how to make use of features like vectorization and broadcasting, when to ditch specialized features in favor of vanilla Python offerings, and a case study where we will use NumPy to write a fast implementation of the K-Means clustering algorithm. ...
For example, say we have an array of 10 observations, each of which consists of three values (called features in this context). We might want to center that data so we have the differences from the mean rather than the raw data itself. Doing so can help us better compare the different...
In NumPy and PyTorch,broadcastingis a way to speed up computation by essentially making the dimensions the correct size by copying data from the smaller dimension until the resulting array is suitable for Linear Algebra addition or multiplication. The one exception: Outer p...
This PR is a split of#21613where I have removed the feature addition of broadcasting. So this PR is only the elementwise and reduction operations for nD COO format. Hopefully this will make it easier to review and get these nice features in. Here is a recap (without broadcasting) of the...
A common data practice is tocenteran array of data. For example, say we have an array of 10 observations, each of which consists of three values (called features in this context). We might want to center that data so we have the differences from the mean rather than the raw data itsel...