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System information

• Have I written custom code (as opposed to using a stock
  example script provided in TensorFlow): Yes

In the documentation it says:

Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]

Neither this explanation nor this example is very clear. I would suggest replacing this with

Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]

Describe the bug

Calling a pipeline with a nonparametric function causes an error since the function transform() is missing. The pipeline itself calls the function fit_transform() if it's present. For nonparametric functions (the most prominent being t-SNE) a regular transform() method does not exist since there is no projection or mapping that is learned. It could still be used f

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.

From the test in #34944:

julia> REPLTests.fake_repl() do in, out, repl
           repltask = @async begin
               REPL.run_repl(repl)
           end
           write(in, "?;\n")
           write(in, '\x04')
           wait(repltask)
       end
search:

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.

Hi,
I thank you very much for your excellent work. However, I endure difficulties understanding your code ; my Java and Python training explain it a bit, but I think it would be great for everyone if it was documented. I mean a short explanation of each - complex - function, its role and its algorithm. I would be especially interested in a documentation of the tracker.
I thank you very much in a

In doc.pyx' s line 590:

 if not self.is_parsed:
            raise ValueError(Errors.E029)

I can still do a good job of chunking by tokenization and pos tagging only, without the full parse. Also in some languages parse isn't available. This will leave more flexibilities to users. I can comment out this in my copy of spacy, but when I update spacy to a new release, I have to chang