Published on: 2025-06-22 19:49:47
What I want from an array language is: Don’t make me think. Run fast on GPUs. Really, do not make me think. Do not. I say NumPy misses on three of these. So I’d like to propose a “fix” that—I claim—eliminates 90% of unnecessary thinking, with no loss of power. It would also fix all the things based on NumPy, for example every machine learning library. I know that sounds grandiose. Quite possibly you’re thinking that good-old dynomight has finally lost it. So I warn you now: My solution is utt
Keywords: dp dumpy np numpy shape
Find related items on AmazonPublished on: 2025-07-01 08:32:58
💡 Inline Dependency Management Install packages right from the notebook: % juvio install numpy pandas Dependencies are saved directly in the notebook as metadata (PEP 723-style), like: # /// script # requires-python = "==3.10.17" # dependencies = [ # "numpy==2.2.5", # "pandas==2.2.3" # ] # /// ⚙️ Automatic Environment Setup When the notebook is opened, Juvio installs the dependencies automatically in an ephemeral virtual environment (using uv ), ensuring that the notebook runs with the correct
Keywords: dependencies install juvio notebook numpy
Find related items on AmazonPublished on: 2025-07-07 04:05:22
They say you can’t truly hate someone unless you loved them first. I don’t know if that’s true as a general principle, but it certainly describes my relationship with NumPy. NumPy, by the way, is some software that does computations on arrays in Python. It’s insanely popular and has had a huge influence on all the popular machine learning libraries like PyTorch. These libraries share most of the same issues I discuss below, but I’ll stick to NumPy for concreteness. NumPy makes easy things easy
Keywords: 10 array np numpy solve
Find related items on AmazonPublished on: 2025-07-07 14:05:22
They say you can’t truly hate someone unless you loved them first. I don’t know if that’s true as a general principle, but it certainly describes my relationship with NumPy. NumPy, by the way, is some software that does computations on arrays in Python. It’s insanely popular and has had a huge influence on all the popular machine learning libraries like PyTorch. These libraries share most of the same issues I discuss below, but I’ll stick to NumPy for concreteness. NumPy makes easy things easy
Keywords: 10 array np numpy solve
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