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System information

• TensorFlow version (you are using): 2.1
• Are you willing to contribute it (Yes/No): No

Steps:

1. Go to a nonexistent page in Keras docs, e.g. https://keras.io/asdfsada so the 404 page is shown
2. Search something
3. You are redirected to https://search.html/?q=something instead of a relative URL

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
X = pd.DataFrame({1: np.random.randint(0, 10, size=400)})
OneHotEncoder().fit(X).get_feature_names([1])

TypeError: unsupported operand type(s) for +: 'int' and 'str'

Similar to https://github.com/pytorch/pytorch/pull/34037/files we can view a complex tensor as a float tensor and pass it to uniform_ used by rand

cc @ezyang @anjali411 @dylanbespalko

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb

trainable_variables = weights.values() + biases.values() doesn't work.
Also if I write trainable_variables = list(weights.values()) + list(biases.values()), I have to turn on tf.enable_eager_execution(), but the training result is wrong, accuracy is ar

Current Behavior:

The the wiki page APIExample, for the python example, the handle api is is run through the TessBaseAPIDelete funciton if the api failed to be initialized whereas for the C example below, this is not the case.

python:

rc = tesseract.TessBaseAPIInit3(api, TESSDATA_PREFIX, lang)
if (rc):
    te

• face_recognition version: latest docs
• Python version: N/A
• Operating System: N/A

Description

the docs say that default value for 'model' is "large". in fact, it is "small", which makes a different. please correct the docs

Could someone tell me if this example comes with any documents about preparing data, training, and testing? Thanks.

Target Leakage in mentioned steps in Data Preprocessing. Train/test split needs to be before missing value imputation. Else you will have a bias in test/eval/serve.

I think we can use either Clang.jl or a simple compile time C script to generate all the values in SuiteSparse_wrapper.c and build a .jl file of constants.

Can address JuliaLang/julia#20985 too.

I think we can also then try and bring back simultaneous support for 32 and 64-bit suitesparse.

This should really help to keep a track of papers read so far. I would love to fork the repo and keep on checking the boxes in my local fork.

For example: Have a look at this section. People fork this repo and check the boxes as they finish reading each section.

Considering the MNIST dataset, wich has 5923 instances of the 0 class in the training set, I'm alittle confused about the following code for detemining the relative errors of the SGD classification model:

row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums

(https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb // In: 67)

Since using `axi

I'm not sure if XGBoost s model is well calibrated with softmax. It would be nice to have a doc with various experiments including random forest, dart etc.

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

What's the ETA for updating the massively outdated documentation?

Please update all documents that are related building CNTK from source with latest CUDA dependencies that are indicated in CNTK.Common.props and CNTK.Cpp.props.
I tried to build from source, but it's a futile effort.

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

fun: 0.31677973007087407
jac: array([ -4.85498059e-05,   1.52435475e-09,  -2.42237461e-08, ...,
       5.36244893e-05,   5.82601644e-05,   7.84943560e-05])
message: 'Max. number of function evaluations reached'
  nfev: 400
   nit: 28
status: 3
success: False
     x: array([  0.00000000e+00,   7.62177373e-06,  -1.21118731e-04, ...,
      -1.12048523e+00,  -1.0043010