Machine Learning vs Deep Learning: What's the Difference?

How does deep learning actually work? Deep learning programs are built on at least three layers of neurons. The first layer is called the input layer. This captures a piece of information about the subject. This is then passed down through the subsequent layers, called channel neurons, into the output layer. Each neuron has a value attached. For example, a dog's paw will trigger a reaction, while cats will not. If an activation threshold is reached that matches the value of the neuron, the appropriate neuron in the output layer activates. This process repeats and adjusts to create a trained network. And because the algorithm only needs unstructured data, texts, and images, it doesn't need human input to characterize objects. By observing repeat patterns, deep learning can cluster inputs and discover hidden patterns and groupings without human intervention. This is known as unsupervised learning. By analyzing thousands of interactions, deep learning can figure out the common keywords and responses that lead to a resolution.