Deep Neural Networks 101
A Deep Neural Network, or DNN, is a type of Artificial Node Network (ANN). Similar to individual neurons in the human brain, nodes in an ANN work together to learn and solve problems based on data inputs.
A DNN has one or more hidden node layers between the input and output layers, meaning that with the right training, it has greater capability to learn to recognize patterns and solve complex problems like we do every day with our brains.
The way a DNN learns is through training on data inputs and machine learning, which is the process a neural network uses to program (or teach) itself. DNNs can be trained on all different types of data, including images, text, videos, sound and more.
To train a DNN, the algorithm is shown many pairs of inputs and labeled outputs. The algorithm then must ‘learn’ for itself what rules can be used to arrive at the correct output for any given input through repeated attempts and continuous feedback about those attempts.
By having a large dataset with plenty of variability, the DNN will eventually be able to generalize—in other words, to accurately handle data it has never seen in training.
For example, if the data set includes 10 pictures of horses, the DNN might correctly identify a new picture of a horse 5% of the time. The other 95% of the time it might think it’s looking at a dog or a table. If the training data set contains 10000 pictures of horses, then the DNN is likely to identify a new picture of a horse almost 100% of the time.
So, just like a human brain, the more information the DNN has been trained on, the better it is able to recognize patterns and make decisions.
Are you still with us? Not too complicated, right? So those are the basics. Now to the fun stuff.