Making data science accessible – Neural Networks

Making data science accessible – Neural Networks

Making data science accessible – Neural Networks
By Dan Kellett, Director of Data Science, Capital One UK

Neural Networks are a family of Machine Learning techniques modelled on the human brain. Being able to extract hidden patterns within data is a key ability for any Data Scientist and Neural Network approaches may be especially useful for extracting patterns from images, video or speech. The following blog aims to explain at a high level how these methods work and key things to bear in mind.

The network consists of different components:

–       Input layer: this reflects the potential descriptive factors that may help in prediction.

–       Hidden layer: a user-defined number of layers with a specified number of neurons in each layer.

–       Output layer: this reflects the thing you are trying to predict. For example; this could be a labelling of an image or a more traditional 0/1 outcome

–       Weights: each neuron in a given layer is potentially connected to every neuron in adjacent layers – the weight sets the importance of this link. At first these weights should be randomized.

Read Also:
An Intuitive Explanation of Convolutional Neural Networks

In a basic neural network, you train the system by running individual cases through one at a time and updating the weights based on the error. The aim is that over time the networks should become attuned to your data, minimizing error. This updating of weights in a basic neural network is an output of a two-way process using feed-forward and back-propagation techniques:

Feed-forward involves processing observations one-at-a-time through the network. Given the weights in place the model should produce a prediction and from this prediction and the actual outcome you can calculate the error in your model for that one observation.

Back-propagation involves taking that error back through the network to adjust the individual weights to better reflect the actual outcome. These new weights are then used for the next observation.

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