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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.

According to
https://github.com/pytorch/pytorch/blob/64847c7f0b3559c6edc40f001619b80c7dc68ef7/c10/util/Exception.h#L479
all usages of AT_ERROR("msg") should be replace with TORCH_CHECK(false, "msg")
There are currently 29 instances of AT_ERROR being used in c10 codebase:

$ grep AT_ERROR c10 -R|grep -v Exception.h|wc -l
29

Most functions in scipy.linalg functions (e.g. svd, qr, eig, eigh, pinv, pinv2 ...) have a default kwarg check_finite=True that we typically leave to the default value in scikit-learn.

As we already validate the input data for most estimators in scikit-learn, this check is redundant and can cause significant overhead, especially at predict / transform time. We should probably a

The current implementation of lyap for complex scalar arguments is incorrect. We can see this by comparing the result obtained using 1x1 matrices vs scalars:

julia> A = (1.0+2.0im)*ones(1,1);

julia> C = (3.0+4.0im)*ones(1,1);

julia> lyap(A, C)  # expected
1×1 Matrix{ComplexF64}:
 -1.5 - 2.0im

julia> lyap(A[1], C[1])  # wrong! 
-1.1 + 0.2im

julia> -C[1] / 2real(A[1])  

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.