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Problem of creating knowledge graph from unstructured data is a well known machine learning problem. Not even a single org has achieved 100% accuracy for completely enriched knowledge graph . I have few findings that will help to kick-start for a person who is new in to this . Before move to findings , i will let you to walk through the problem of building knowledge graph from unstructured corpus . Lets consider this scenario . Suppose we have very small corpus : "Apple was founded by Steve jobs and current CEO is Tim Cook. Apple launched several products like Ipad, iphone , MAC etc. " Corpus may be very complex sentences also . Problem is how can we build a knowledge graph out of this unstructured corpses . If we create generic knowledge graph , then our system should be able to provide answers like "who founded Apple ?" , " What are products launched by Apple ?" etc . Few techniques to create knowledge graph : 1.) Supervised Technique : Supervised mod

Below are the steps to setup your development environment  for Deep learning : 1.) Download and Install Anaconda from here : https://www.continuum.io/downloads 2.) Create a conda environment for data science development so that it doesn't affect the other install components .     conda create -n tensor_keras_py3.5 python=3.5 pandas scikit-learn jupyter matplotlib 3.) Activate the created environment     source activate tensor_keras_py3.5 4.) Install tensorflow inside activated env.     pip install tensorflow 5.) Install keras inside activated env.    pip install keras 6.) Install opencv inside activated env.    pip install opencv-python 7.) Install IMUTILS  pip install imutils Test your environment 1.) Type ipython in the shell  , which should open ipython console . 2.) Type import tensorflow,keras  , it should reply using tensorflow backend  Switching keras backend from Tensorflow to theano keras backend is set in a hidden file stored