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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

(e.g. for links and images), because some of these examples are now being rendered in the docs.

Added by @fchollet in requests for contributions.

See https://pytorch.org/docs/master/generated/torch.matrix_exp.html?highlight=matrix_exp#torch.matrix_exp.

It would also be nice if the mathematical expression related to the signature.

cc @brianjo @mruberry

It appears that the docs for Logistic Regression differ based on solvers and penalties. The "penalty" parameter states that "The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties," while the "solver" parameter states that "‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty" (attaching some screenshots). This was actually a little unclear to me, as I wasn't sure if the n

Julia 1.6.0-rc3

runtests.jl:

using Test

d = Dict("hello" => "world")
@test keys(d) == ["hello"]

Test output:

     Testing Running tests...
Test Failed at /Users/fons/Documents/Pluto.jl/test/runtests.jl:4
  Expression: keys(d) == ["hello"]
   Evaluated: ["hello"] == ["hello"]
ERROR: LoadError: There was an error during testing
in expression starting at /Users/

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.