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

Description of your problem

Interpolated Docs are missing sample plot. One should be added
https://docs.pymc.io/api/distributions/continuous.html#pymc3.distributions.continuous.Interpolated

Please provide any additional information below.
See example from Normal plot for

Our default placeholders sometimes cause confusion, like in this thread. Perhaps different placeholders, like "[email protected]" and "YourTokenHere" will help with that.

Another thread: https://www.coursera.org/learn/practical-rl/discussions/all/threads/jxK8jdhzQ6iSvI3Yc-Oouw

When passing a two-dimensional input layer to LSTMLayer, it will break with an uninterpretable error message:

>>> lasagne.layers.LSTMLayer((10, 20), 30)
[...]
TypeError: 'numpy.float64' object cannot be interpreted as an index

The reason is that np.prod(()) returns 1.0 as a numpy.float64 instance when computing num_units: https://github.com/Lasagne/Lasagne/blob/master/lasagne

首先感谢分享程序。
请问在6x6 四子棋的训练过程中，有没有调节learning_rate或者其他参数？
程序里的c_puct=5 , 温度t=1，学习率 0.002，batch_size 512 , deque最大长度10000, kl-targ=0.02 ,epochs=5
我使用你程序里的预设参数 tensorflow训练6x6 四子棋 ，loss下降到2左右就无法下降了，调节学习率也没成功。。。求帮助解答，谢谢

另外，不明白explain_var_old这个参考数值的意义。

This was reported to me by @max-andr. Most of the differences are actually explicitly mentioned in comments in our implementation, but we should check again if we can match the reference implementation more closely and possible mention deviations in the docs, not just in comments.

@max-andr might create a PR to fix this

This issue is related to #412 and #470 . Now that we have a proper and easy way to build the documentation, all classes/functions should go in there. There is also quite some work needed because some docstrings are not formatted correctly (mostly newlines and indentation issues). I made the skeleton of the docs, what's left is making it big and pretty.

To start, see those files:

• https://gi

Deep Learning for Text-to-Speech Synthesis, using the Merlin toolkit

This tutorial will combine the theory and practical application of Deep Neural Networks (DNNs) for Text-to-Speech (TTS). It will illustrate how DNNs are rapidly advancing the performance of all areas of TTS, including waveform generation and text processing, u

Though you haven't yet done an extensive hyperparameter search, might you give us one or two examples of hyperparameter settings you have used?

Keras-rcnn was written to be compatible with a number of third-party frameworks and services like Apple’s Core ML framework that enables developers to embed Keras models into their iOS applications. We should document how an Apple developer can create, train, and export their model to their Core ML-compatible iOS application.