
Effective Tensorflow - adamnemecek
https://github.com/vahidk/EffectiveTensorflow?
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0xbear
But _why_ use TF when you have PyTorch which is just as powerful, runs
noticeably faster for most workloads, _and_ is easy to understand? What are
you gaining by using TF these days?

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jamesmishra
I think at the moment, TensorFlow has a better tooling and deployment story
with tools like TensorBoard[1], Tensor Processing Units in Google Cloud[2],
distributed training[3], and deploying to mobile apps[4].

As time passes, PyTorch will probably get better at all of these things, but
the core team and surrounding community need to make it a priority.

Meanwhile, Google's investment in TensorFlow will continue, and there's no
sign that it will be a _bad_ bet for large-scale deployments... even if
PyTorch becomes the favored system for prototyping.

[1]:
[https://www.tensorflow.org/get_started/summaries_and_tensorb...](https://www.tensorflow.org/get_started/summaries_and_tensorboard)

[2]: [https://www.servethehome.com/google-cloud-tpu-details-
reveal...](https://www.servethehome.com/google-cloud-tpu-details-revealed/)

[3]:
[https://www.tensorflow.org/deploy/distributed](https://www.tensorflow.org/deploy/distributed)

[4]: [https://www.tensorflow.org/mobile/](https://www.tensorflow.org/mobile/)

~~~
0xbear
TPU will be everywhere next year in the form of NVIDIA Tesla V100. And FB is
clearly getting much better ROI. It's not even close.

~~~
david-gpu
Google's TPU and Nvidia's GPUs are competing products.

Source: works at NVidia.

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RSchaeffer
"The most striking difference between Tensorflow and other numerical
computation libraries such as numpy is that operations in Tensorflow are
symbolic. This is a powerful concept that allows Tensorflow to do all sort of
things (e.g. automatic differentiation)"

Sorry if this is a stupid question, but can someone explain how symbolic
operations allow automatic differentiation or link me to a good explanation?

~~~
make3
"Symbolic operations" is a fancy way of saying you build up the full
mathematical equations with operator overloading instead of computing the
results at each individual steps. Having the full equations then allows to
differentiate with regards to a variable of your choice included in that
equation

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rockyj
Something like lazily calculating/evaluating an operation, right?

~~~
Q6T46nT668w6i3m
Yep (from a compilation perspective)

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ahartmetz
This is very good documentation. In my technical writing activities I have
learned that there are two kinds of bad documentation: Math textbooks ("Prove
X. Lemma: ..." [Why are we proving this anyway?]) and cooking recipes ("Do a,
b, c, the end" [What if I need to cook something else?]) - too little practice
or too little theoretical foundation. Sometimes both are present but too
disjoint, or the theory is badly structured. This one does a great job at
introducing the foundations, in the right order, and immediately showing what
they mean to you in practice.

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thess24
For those who use Tensorflow regularly:

What 'type' of tensorflow do you recommend for a project starting today --
tf.slim, tf.contrib.keras, 'raw' tensorflow, keras with tensorflow?

I am building a production model so was probably going to use tensorflow
because I like the tooling (tensorboard), and ability to write once for
production and research.

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zebra9978
is anyone using tensorflow or caffe2 on the mobile ? We are trying to build
something on the android.. but it seems there are no real-life deployments
using caffe2 or tensorflow on the mobile.

~~~
W0lf
There are clear instructions [1] for mobile (iOS and Android) in the official
TF Repository.

[1]
[https://github.com/tensorflow/tensorflow/tree/master/tensorf...](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/makefile)

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ibm-devex
I am curious why someone wouldn't use IBM Watson for this? It seems it's
better suited for production and serious use cases than PyTorch or TF.

