cc @ezyang @anjali411 @dylanbespalko

In the PCA section there is the following quote:

We see that these 150 components account for just over 90% of the variance.

While not inaccurate (150 componen

Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

Picking up from #15731, there are many places in numpy where we do something like:

try:
    something_which_throws_typeError
except TypeError:
    raise ValueError("a clearer explanation of what went wrong")

It would produce a marginally clearer error if we could change these to

try:
    something_which_throws_typeError
except TypeError as e:
    raise ValueEr

If you join Dask DataFrame on a categorical column, then the outputted Dask DataFrame column is still category dtype. However, the moment you .compute() the outputted Dask DataFrame, then the column is the wrong dtype, not categorical.

Tested on Dask 2.14.0 and Pandas 1.0.3
This example where the category type looks like a float, so after .compute(), the dtype is float.

import dask.d

Describe the bug
I try to run tensorboardX/examples/demo_graph.py for jupyter notebook (launched by anaconda navigator) and I get the error seen at Additional context.

I just copy paste the code to notebook from Github.

Minimal runnable code to reproduce the behavior
class SimpleModel(nn.Module):
def init(self):
super(SimpleModel, self).init()

Sphinx (2.2.1 or master) produces the following two kinds of warnings in my environment.

duplicate object description

I think cross refs to such object is ambiguous.

autosummary: stub file not found

There are

• chainer.dataset.Converter base class and
• chainer.dataset.converter decorator.

Therefore the filesystem has to allow to store `chainer.dataset.Conver

Feature request

It'd be useful to have the amount of memory available on a machine added to the numba -s output. This is useful to know for cases where e.g. swapping might be occurring.

This is a good first issue, work involves:

1. Find a way to get the total available memory using Python standard libraries or NumPy.
2. Print the information in 1. in this section: https://github.com

No.9's solution may be miscopied from No.10's solution.
I suggest the solution should be revised from Before to After.

9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)

hint: reshape

Before

nz = np.nonzero([1,2,0,0,4,0])
print(nz)

After

np.arange(0,9).reshape(3,3)

Description

Trax is a library for deep learning that focuses on sequence models and reinforcement learning. It combines performance with code clarity and maintained documentation and tests.
...

Sorry to bother, I'll be brief. I don't think the "maintained documentation" part of the statement is true (yet?). I like the work and I respect every project that goes deep down on neural network

In my opinion, some people might not be able to contribute to CuPy because of not having an NVIDIA GPU. But they might not know that we can build a development env on google colab(As I did here).

import os
from google.colab import drive
drive.mount('/content/drive')
os.chdir("/content/drive/My Drive/")

!git clone ht

In numpy we have this:

>>>np.unravel_index(0, [1,32,32,1])
(0, 0, 0, 0)

>>>np.unravel_index([0,1,2], [1,32,32,1])
(array([0, 0, 0]), array([0, 0, 0]), array([0, 1, 2]), array([0, 0, 0]))

But in xtensor we can only use unravel with one index. Also we'll get by this not an xarray of
dimensions, but this:

"xt::get_strides_t<std::vector<int, std::allocator>>"

Why is it

Support error function and fresnel integrals in https://docs.scipy.org/doc/scipy/reference/special.html#error-function-and-fresnel-integrals, those are not universal functions may not need to be supported.

MCVE Code Sample

# Your code here
import numpy as np
import xarray as xr

data = np.zeros((10, 4))
example_xr = xr.DataArray(data, coords=[range(10), ["

The random_walk function is not a direct equivalent to the RandomWalker class method. A strict equivalent would be this:

def random_walk(n):
    position = 0
    for i in range(n):
        yield position
        position += 2*random.randint(0,1)-1

It is still not much faster, but it's a more fair comparison.

Problem Description

The function description documentation for alphalens.performance.factor_alpha_beta says "Compute the alpha (excess returns), alpha t-stat (alpha significance), and beta (market exposure) of a factor. "

The return documentation says "A list containing the alpha, beta, a t-stat(alpha) for the given factor and forward returns."

I am assuming "a t-stat" means "and t-sta

在基础篇 NumPy 的安装这一则内容中的表述并不太准确，如下：
基础篇 NumPy 的安装

对于 Windows 用户而言，直接在 cmd 或者 power shell 中输入 pip install numpy 命令后，安装的并不是完整版的 NumPy.
没有 mkl 支持的 NumPy 没法生成窗函数（至少是这个，别的函数应该也有不支持的）。

建议 Windows 用户从 [Unofficial Windows Binaries for Python Extension Packages](http://www.lfd.uc