If you wanted to listen to someone speaks three hours straight about deep learning, You could have done so by the comfort of your house.

But you are here! Physically!

So...

This tutorial is extremely hands-on! You are strongly encouraged to play with it yourself!

• git clone https://github.com/ypeleg/keras_rl_tutorial

• You might think that the goal of this tutorial is for you to play around with Deep Learning. This is wrong.

• The Rreal Goal of the Tutorial is To give you the Flexibility to use this In your own domain!

Therefore, this is by far the best option if you can get it working!

• Anyone can use the colab.research.google.com/notebook website (by clicking on the icon bellow) to run the notebook in her/his web-browser. You can then play with it as long as you like!
• For this tutorial:

Anyone can use the mybinder.org website (by clicking on the icon above) to run the notebook in her/his web-browser. You can then play with it as long as you like, for instance by modifying the values or experimenting with the code.

• Either directly in GitHub: ypeleg/ExpertDL;
• Or on nbviewer: notebooks.

• Part I: Introduction

  • Intro Keras

    • Functional API

  • Reinforcement Learning

    • Intro
    • Bandit
    • Q learning
    • Policy Gradients

You are probably famillier with this.. so..

In this tutorial many of the irrelevant details are hidden in a special file called "tachles.py". Simply go:

import tachles

This tutorial requires the following packages:

• Python version 3.5

  • Other versions of Python should be fine as well.
  • but.. who knows? :P

• numpy version 1.10 or later: http://www.numpy.org/

• scipy version 0.16 or later: http://www.scipy.org/

• matplotlib version 1.4 or later: http://matplotlib.org/

• pandas version 0.16 or later: http://pandas.pydata.org

• scikit-learn version 0.15 or later: http://scikit-learn.org

• keras version 2.0 or later: http://keras.io

• tensorflow version 1.0 or later: https://www.tensorflow.org

• ipython/jupyter version 4.0 or later, with notebook support

(Optional but recommended):

• pyyaml
• hdf5 and h5py (required if you use model saving/loading functions in keras)
• NVIDIA cuDNN if you have NVIDIA GPUs on your machines. https://developer.nvidia.com/rdp/cudnn-download

The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures.

I'm currently running this tutorial with Python 3 on Anaconda

!python --version

1. Create the keras.json (if it does not exist):

touch $HOME/.keras/keras.json

2. Copy the following content into the file:

{
    "epsilon": 1e-07,
    "backend": "tensorflow",
    "floatx": "float32",
    "image_data_format": "channels_last"
}

!cat ~/.keras/keras.json

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import sklearn

import keras

import numpy
print('numpy:', numpy.__version__)

import scipy
print('scipy:', scipy.__version__)

import matplotlib
print('matplotlib:', matplotlib.__version__)

import IPython
print('iPython:', IPython.__version__)

import sklearn
print('scikit-learn:', sklearn.__version__)

import keras
print('keras: ', keras.__version__)

# optional
import theano
print('Theano: ', theano.__version__)

import tensorflow as tf
print('Tensorflow: ', tf.__version__)