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Reviews and mentions
 Running tensorflow/etc. inside vms...? Is it workable for performance?

I'm going to start with the tulpa thing. I want to know about the tulpa thing. Can you tell me about the creation process and how you did it?
There's something called 'tulpas' in Python called Tensorflow.

Tensorflow GPU yields a lower accuracy then Tensorflow CPU
Hello! My group is training a model based on ResNet50 architecture pretrained with the ImageNet dataset. We have noticed that when training the model on tensorflowcpu 2.6.0, it yields higher accuracy than tensorflowgpu 2.0.0. I am unaware as to why this happens. I have found this link: https://stackoverflow.com/questions/43221730/tensorflowsamecodebutgetdifferentresultfromcpudevicetogpudevice and this: reduce_mean is not numerically accurate on GPU · Issue #3775 · tensorflow/tensorflow · GitHub . It is still unclear to me as to why such happens. If I install a higher version of tensorflowgpu, like tensorflowgpu 2.6.0, will there be changes in the resulting accuracy?
 The State of Machine Learning Frameworks in 2019 (2019)

Tensorflow 1 vs Tensorflow 2
Have you read the release notes?

Top 10 Python Libraries for Machine Learning
Website: https://www.tensorflow.org/ GitHub Repository: https://github.com/tensorflow/tensorflow Developed By: Google Brain Team Primary Purpose: Deep Neural Networks

Load Faster RCNN Object Detection net with Tensorflow 2.5.0 and OpenCV 4.3 DNN package
[1]: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph_test.py [2]: https://leimao.github.io/blog/SaveLoadInferenceFromTF2FrozenGraph/
 Why does Microsoft have a higher P/E ratio that Google and Apple? Is Microsoft expected to grow more than the other two?

Should you learn Julia or Python for Machine Learning?
But, now we have to get used to Python's library of Machine Learning packages: tensorflow, numpy, matplotlib, and finally pandas
 Release TensorFlow 2.6
 TensorFlow 2.6

Dynamical Isometry and Mean Field Theory: How to Train 10kLayer Vanilla CNNs [pdf]
This paper is one of my all time favorites.
It shows that extremely deep vanilla CNNs  without the use of batch normalization or residual connections  can be trained simply by using a DeltaOrthogonal weight initialization scheme and appropriate activation function.
The DeltaOrthogonal initialization scheme is derived theoretically by developing a mean field theory for signal propagation which characterizes the conditions for dynamical isometry. Ultradeep CNNs can train faster and perform better if their inputoutput Jacobians exhibit dynamical isometry, namely the property that the entire distribution of singular values is close to 1. Put another way, dynamical isometry is a necessary condition for signals to flow both forward and backward through the network without attenuation. A variety of pathologies such as vanishing/exploding gradients make training such deep networks challenging  mean field theory is a powerful tool that offers solutions to these challenges.
The authors demonstrate experimentally that DeltaOrthogonal kernels outperform existing initialization schemes for very deep vanilla convolutional networks. They also find strikingly good agreement between theoretical and experimental results. One of the most astonishing findings IMO is that for networks initialized using this scheme the learning time measured in number of training epochs is independent of depth.
> Our results indicate that we have removed all the major fundamental obstacles to training arbitrarily deep vanilla convolutional networks. In doing so, we have layed the groundwork to begin addressing some outstanding questions in the deep learning community, such as whether depth alone can deliver enhanced generalization performance. Our initial results suggest that past a certain depth, on the order of tens or hundreds of layers, the test performance for vanilla convolutional architecture saturates. These observations suggest that architectural features such as residual connections and batch normalization are likely to play an important role in defining a good model class, rather than simply enabling efficient training.
Here is a link to the ConvolutionDeltaOrthogonal initializer in tensorflow [1].
[1] https://github.com/tensorflow/tensorflow/blob/d287ff3d95c06b...

TensorFlow: Distutils
If you run python c 'print "Python Error"' then you can find the bug, just like this: https://github.com/tensorflow/tensorflow/issues/4929
You can install tensorflow.py from github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tensorflow.py
Stats
tensorflow/tensorflow is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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