A Descent into Deep Learning

Next, we'll see if we can tackle a problem that may have real-world implications for deep learning. While I jest about the awesome power of neural networks for classifying flowers, there are some cool ways to show that the long arm of deep learning extends beyond the casual gardener's domain. Specifically, I'd like to show that deep learning can be used in a healthcare setting. There are plenty of ways that I might be able to show this, but perhaps the message would be most effective if I could tackle something relevant. So, we'll be tackling a prediction task related to heart disease, which, according to this image, is rather important: Heart disease affects most people whether it's directly or through the affliction of a loved one. It is the cause of death for 25% of Americans and, if your household is as pork-chop-laden as mine, probably has affected you as well. So, what then can we do to show that deep learning is relevant to heart disease? Well, it...

In the previous post, we fiddled with data and set out for an ambitious task of classifying plants. We ended that post after having downloaded, prepared, and partitioned our data, resulting two non-overlapping sets of data: a test set and a training set. We'll continue now by building, training, and evaluating a neural network to classify these flowers. First, we'll need to separate the values that we'll use as input to the network from the values we're intending to use as the output from the network. In other words, we need to place all the sepal and petal widths and lengths into a bag that the network can read, and all the species corresponding to the individual sets of sepal and petal widths and lengths into another bag. That way, when the network trains, it only takes into account the 4 inputs. When it produces an output, we can use the other bag (the one with species inside) to see if the network has predicted the species correctly. Before doing that, we'...