# AI News, Artificial Neural Networks for Beginners 5

- On Sunday, June 3, 2018
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## Artificial Neural Networks for Beginners 5

Deep Learning is a very hot topic these days especially in computer vision applications and you probably see it in the news and get curious.

Today's guest blogger, Toshi Takeuchi, gives us a quick tutorial on artificial neural networks as a starting point for your study of deep learning.

In the remaining columns, a row represents a 28 x 28 image of a handwritten digit, but all pixels are placed in a single row, rather than in the original rectangular form.

The app expects two sets of data: The labels range from 0 to 9, but we will use '10' to represent '0' because MATLAB is indexing is 1-based.

Then you will partition the data so that you hold out 1/3 of the data for model evaluation, and you will only use 2/3 for training our artificial neural network model.

Individual neurons in the hidden layer look like this - 784 inputs and corresponding weights, 1 bias unit, and 10 activation outputs.

If you look inside myNNfun.m, you see variables like IW1_1 and x1_step1_keep that represent the weights your artificial neural network model learned through training.

The general rule of thumb is to pick a number between the number of input neurons, 784 and the number of output neurons, 10, and I just picked 100 arbitrarily.

It looks like you get the best result around 250 neurons and the best score will be around 0.96 with this basic artificial neural network model.

As you can see, you gain more accuracy if you increase the number of hidden neurons, but then the accuracy decreases at some point (your result may differ a bit due to random initialization of weights).

As you increase the number of neurons, your model will be able to capture more features, but if you capture too many features, then you end up overfitting your model to the training data and it won't do well with unseen data.

You now have some intuition on artificial neural networks - a network automatically learns the relevant features from the inputs and generates a sparse representation that maps to the output labels.

In this example we focused on getting a high level intuition on artificial neural network using a concrete example of handwritten digit recognition.

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