Have you ever tried to get something novel out of a computer? I can ask you to make up a story or look at a picture and tell me what's in it. How would you make a computer program behave like this in contrast to the digital storage and transfer unit we've used them for these past 30+ yrs?
If you had perfect knowledge and all the time in the world, you could write all the rules by which a computer program would need to operate. Of course, if you had all the knowledge to define the operational rules, you wouldn't need the computer to do anything! So what do you do if you need a computer to function in complex ways (making predictions, classifications, optimizing processes, generating content, responding to interactions, performing robotic controls), but don't have all the heuristic rules defined?
You build an algorithmically-based application that can learn the rules, find the pattern, or determine the signal, from data that comes from the domain space in question. You set up the training such that it iterates incredibly fast and with a great number of cycles (we call them epochs) to provide the "experience" to incrementally train the model in a process that would not be possible in a human lifetime.
When we build these algorithmic architectures in layers, we create deep learning models that can learn features (for example, dogs have tails and cars have wheels) and these learned features are powerful! What we really find in Python Deep Learning Projects is that we can ask profound questions not possible before. It's these questions that drive our deep learning technologies to solve problems that range from healthcare diagnostics in radiology to cancer screening. Deep learning applications drive chatbot experiences, facial recognition, autonomous vehicles, recommendation engines, and marketing tech. The hard sciences of physics, biology, and chemistry are incorporating deep learning skills training just as they have in the past with regard to statistics and microscopes.
