This is my first post regarding artificial intelligence (AI). But I promise to include as much as I can from understanding Simple Neural Network (NN) to deep learning through little theoretical but lots of practical implementations. I will also include simple projects where possible. Lets begin building then.
I like Python a lot so most of the works will be done in Python. Later on I am hoping to develop them in Java and Scala.
To learn NN we will not be using any NN libraries but some mathematical libraries, ie. numpy.
Learn basics of Numpy HERE .
To begin building NN which is supposed to mimic how our brain works, we have to understand little bit of our own Brain.
An averaged sized Brain includes of 100 billion neurons connected by synapses. Neurons are the basic unit of brain which plays major role for all the tasks done by brain. Blah blah blah… its better you go through this well written article (A Basic Introduction To Neural Networks).
This tutorial we will be building a Artificial unit of this very Neuron. I consider you know matrices which we will be using as mathematical foundation for building NN with numpy.
Our simple ANN will include three inputs and a output. (Input: 3, Output: 1). This neuron we will build should classify a basic problem of classification. We will use various different training algorithms to train our neuron for classification.
So our neuron will have very small dataset for training (a deeplearning model will need very very large dataset for better performance) which will be enough in this problem.
| Example# | Input A | Input B | Input C | Output Y |
| 1 | 0 | 0 | 1 | 0 |
| 2 | 1 | 1 | 1 | 1 |
| 3 | 1 | 0 | 1 | 1 |
| 4 | 0 | 1 | 1 | 0 |
| 5 | 1 | 0 | 0 | ??? (1 expected) |
So what will be the output for the last data row (Row #5)?.
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