Getting started with Tensorflow

Tensorflow is an open source library created by Google for deep learning tasks. The library mainly works with matrices operations, it represents the operations between matrices and data by a graph that shows the dependency between the tasks. The edges (called tensors) between the nodes are the output of the operations.

There are many advantages of using tensorflow such as it reduces the development time by avoiding hard-coding some mathematical operations, also it supports GPU.

Recently I have added a solution for XOR learning problem in my notebook using tensorflow. It was the first time for me to use tensorflow in Machine Learning, and I think it won’t be the last time as I find it an awesome library that I could use.

This is a model from my machine learning masters course’s assignment.

xor

You can check the solution here https://github.com/AhmedHani/CS624-ML/blob/master/Assignments/xor_learning.ipynb

[Kaggle] Poker Rule Induction

I wrote a note http://nbviewer.ipython.org/github/AhmedHani/Kaggle-Machine-Learning-Competitions/blob/master/Easy/PokerRuleInduction/PokerRuleInduction.ipynb about Poker Rule Induction problem, the note explains the problem description and the steps I used to solve it.

It is considered a good problem for those who want to start solving at Kaggle and know about some Machine Learning libraries in Python that are commonly used when solving at Kaggle.