Deep learning uses neutral networks and algorithms based in analog to simulate human decision-making processes faster and more accurately. But what does that really mean and how does deep learning work? Remember, deep learning is a form of machine learning AI that uses algorithms in analog. Imagine you are looking at an image composed of 50 million pixels. Your job is to locate the picture of a house in the image. Of course, it would take us a very long time to find that little house in a mound of millions of pixels. This is where machine learning comes in to help us. It can develop engineering and design techniques that make the system search for a window or a roof. It does this by developing specific algorithms that automatically search for these items. But this is also not the ideal approach. Deep learning—as a specialized type of machine learning—provides greater assistance here. Deep learning relies on a pool of data and complex algorithm that automatically learns what to be looking for from the data. The difference between these AI approaches is that deep learning methods make the system learn by itself from the entire data pool. The former approach instead relies on data inputted by a human that the system must then learn from. Thus, it is always more efficient for the system to be learning by itself as opposed to learning from human design and input. The results can be generated faster and with greater accuracy. This is why deep learning is a more focused and sophisticated form of machine learning.