This repo contains a quick overview of applying deep learning using tensorflow. It was created for Gaza DevFest17 as a code lab demonestration.
In order to use tensorflow and applying basic deep learning example we are going to use a pre-configured docker instance. This will help us getting a ready to use environment.
- To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
- Make sure that Hyper-V is disabled if it is installed on your pc.
- If you are using Windows 10 you can directly download docker and install it from Docker Store.
- Otherwise, you can download and install Docker Toolbox.
- After installation is done, open new cmd window and type
docker ps
. If you encountered an error running this command, you should follow these steps. - If the above command does not work and you get error message says
Error checking TLS connection:..
, try to run this commanddocker-machine restart
then run the above step. - Finally, it is worth to spend few minutes on reading Get Started Tutorial
- Open new cmd window and run
docker image ls
. It should list available docker images. - To install a tensorflow docker image we need first to download it. run
docker pull tensorflow/tensorflow
. - After downloading and unpacking is done, you can start new container by running this command
docker run -it --rm --name="tf" -p 8888:8888 -p 6006:6006 tensorflow/tensorflow
- Copy the link generated and paste it on new browser window. It should point to a jupyter notebook working directory.
- If link is not working try to replace
localhost
by192.168.99.100
- Now you are ready to rock 👍
- Find My presentation about Neural Network and Deep Learning at SlideShare
- Code Sample for Simple Linear Model Code
-
Get started with Tensorflow
-
Tutorials
- TensorFlow Tutorial and Examples for Beginners repo
- TensorFlow Tutorials with YouTube Videos
-
Basic Introduction to Neural Networks and Deep Learning, Article1 Article2
-
Machine Learning course tought by the best.
-
Deep Learning course that covers almost every model of use with good introduction.