Alrighty then, let’s dive into this term “Deep Learning”, or as some like to call it, DL. Imagine you’re teaching a toddler, let’s call him Johnny, to become a champ cyclist. You wouldn’t just let Johnny practice in the backyard, would you? Nope, you’d take him to the park, let him tackle the hilly landscapes, maneuver around curvy pathways, and perhaps let him experience a muddy trail or two. That’s exactly how Deep Learning works!
Deep Learning is like a bright pupil in the grand classroom of Machine Learning (ML). It specifically employs a special structure known as neural networks, akin to our brain’s own structure, with loads (and i mean LOADS) of layers. These layers give it the ‘deep’ tag. Think of each layer as a different terrain Johnny practices on. Each one helps him learn a new aspect of cycling.
The trick to Deep Learning is that it’s a data glutton. It gobbles up vast quantities of data, using it to learn and improve, just like Johnny riding through all kinds of terrains to get better at cycling.
To put it simply, Deep Learning is an advanced technique under the umbrella of Machine Learning. It’s like an eager student constantly learning and improving, mastering its skills by processing a plethora of data. Kinda cool, isn’t it? Now that’s one clever machine-child we got there!