
TensorFlow Tutorials - bootload
https://github.com/pkmital/tensorflow_tutorials
======
stared
I really like their high-level tutorials as in here:
[https://www.tensorflow.org/versions/0.6.0/tutorials/](https://www.tensorflow.org/versions/0.6.0/tutorials/)
(with descriptions and general intro to neural networks, not only - a specific
library).

There are also Jupyter Notebooks:
[https://github.com/tensorflow/tensorflow/tree/master/tensorf...](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/udacity)
(used as exercises in the Udacity course:
[https://www.udacity.com/course/deep-learning--
ud730](https://www.udacity.com/course/deep-learning--ud730)).

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abledon
This is a great start.

However, I love to attach narratives to problems, and have always enjoyed the
coding competition questions where they tell a little scenario (albeit
contrived) that usually have to do with something like a farmer and her cows
in a barn or bees flying to pollen.

It seems to somehow engage my mind more with the problem.

Anyways, It would be great if there was some sort of 'problem description' and
a background story context to really get the learner excited about applying
the machine learning tool to solve real (fictional) life problems that are
affecting real (fictional) people.

And just to dream, maybe a comic edition like codecomics does with react, or
learning mysql with nostarchpress manga comics.

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sgt101
These look like machine learning cases - does anyone have any other examples -
network design? Crypto?

~~~
nrooot
Some other applications: [https://github.com/aymericdamien/TensorFlow-
Examples](https://github.com/aymericdamien/TensorFlow-Examples)
[https://www.tensorflow.org/versions/0.6.0/tutorials/pdes/ind...](https://www.tensorflow.org/versions/0.6.0/tutorials/pdes/index.html)
[https://www.tensorflow.org/versions/0.6.0/tutorials/mandelbr...](https://www.tensorflow.org/versions/0.6.0/tutorials/mandelbrot/index.html)

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akerro
Does anyone have experience how TensorFlow work on Raspberry pi? I have an
idea for a simple robot-car that's learns itself how to drive and and
transport sensitive things (say, full glass of water). Would it put too much
heavy load operations on Raspi 2?

~~~
dharma1
I would use NVidia Jetson TK1 or X1 instead, you can run same CUDA accelerated
ML frameworks as elsewhere. Runs Ubuntu. TK1 is about $190 with 192 CUDA cores
and enough IO for whatever you need.
[http://elinux.org/Jetson/GPIO](http://elinux.org/Jetson/GPIO)

Train on a desktop/cloud GPU and use the trained network on the Jetson, you
should get realtime perf for most simple autonomous driving related stuff.

Look into ROS and Ardupilot Rover as well for a head start into autonomous RC
cars.

Here's a tutorial on getting Tensorflow running on Jetson -
[http://cudamusing.blogspot.co.uk/2015/11/building-
tensorflow...](http://cudamusing.blogspot.co.uk/2015/11/building-tensorflow-
for-jetson-tk1.html)

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chrisconley
This is great!

If anyone's interested, yesterday we put up a fairly detailed guide to
installing TensorFlow on EC2 GPU instances with Python 3.4.

[http://eatcodeplay.com/installing-gpu-enabled-tensorflow-
wit...](http://eatcodeplay.com/installing-gpu-enabled-tensorflow-with-
python-3-4-in-ec2/)

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ioab
I'm trying to read the official docs for a computer vision gp and my mind
froze. Will try these ones. Thanks :)

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wrong_variable
Half the tutorial is about how to set up virtualEnv and other config.

This is why I hate developing in python. I will just wait for go and node.js
to come up with their own APIs for tensor flow.

~~~
mzahir
Use the docker image instead -
[https://www.tensorflow.org/versions/0.6.0/get_started/os_set...](https://www.tensorflow.org/versions/0.6.0/get_started/os_setup.html#docker_install)

~~~
cookingrobot
I'd successfully finished the Andrew Ng Coursera class using Matlab on my old
MacBook Air, and moved on to TensorFlow. Turns out Docker is just too much for
my 2 Gb Mac - it totally hangs. I got it working pretty easily on a web IDE
codeenv, but it has limitations and I couldn't load the sample files or use
ipython notebook. I'd love to run through these tutorials on a remote hosted
environment with minimal configuration work. Anyone have advice?

