Add readme and requirements.txt file

Author: sque

README.md

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+## My-NN  ⋮∶• Playground for neural networks
+
+A simple and self-descriptive implementation of neural networks with support
+of different topology, activation functions and optimization strategies. 
+
+A useful tool if you are interested to understand the intuition of neural networks through experimentation.
+
+### Activation functions
+
+Library provides abstract interface for activation functions as long it is possible to provide a forward computation 
+and a derivative estimation. The package is shipped with 3 implementations of activation functions:
+
+* `ReLU`
+* `Tanh`
+* `Sigmoid`
+
+### Optimization strategy
+The optimization strategy is a core component for any optimization problem. Depending the strategy
+the training can be faster, more effective. For this reason it is very beneficial to understand the mechanics behind
+each algorithm before choosing one. Sebastian Ruder has published [a very nice blog post](http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms)
+where he presents in detail different optimization algorithms.
+
+MyNN is shipped with 3 different implementation of generic optimization strategies.
+
+* `GradientDescent`: Classic gradient descent that works in batches
+* `GradientDescentMomentum`: It works as the classic gradient descent but takes
+into account the previous grads in order to keep a momentum
+* `AdaptiveGradientDescent` (**Default**): A gradient descent that adapts learning rate per
+optimized parameter. 
+
+
+### Installation
+MyNN can **only** work on `python >= 3.6`. It is proposed to use `virtualenv` to perform
+installation.
+
+```sh
+$ virtualenv -ppython3.6 venv
+$ source venv/bin/activate
+```
+
+You can install the dependencies using `requirements.txt` file.
+```sh
+$ pip install -r requirements.txt
+```

requirements.txt

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+numpy~=1.13.3