Deep Learning vs. Machine Learning: What’s the Difference?
How deep learning and neural networks work and how they're different from just machine learning.
Deep learning is a type of artificial intelligence used for image and sound analysis and other types of pattern recognition using an artificial neural network. Watch now:
A deep learning model users math to simulate the neural network structure of the human brain. But it’s more than trying to replicate a model of the brain. It’s a subset of machine learning that attempts to train computers to identify and understand things in context.
So if we gave a neural network large amounts of data in the form of a training data consisting of a several hundreds of photos of cats, the deep learning model would eventually learn how to identify a cat.
As the machine learning models learn a pattern it can also learn variations of that pattern. This comes in handy with applications like voice recognition as the system learns to understand what you say when you say it.
Finance is a key industry that is going to see a lot of benefits of deep learning since it helps companies more fully understand the customer and monitor account transaction for anything that seems off or odd. Deep learning and neural networks have completely changed the way financial firms detect identity theft and fraud.
One pitfall of deep learning is when the training data is too narrow and so the machine sees a very narrow view of the things it’s trying to identify and find. This is called over-training and makes it a lot harder to debug this model than some of the other popular machine learning models.
On the positive side, deep learning allows the machine to learn new concepts on top of what it’s already learned. Like I said before, each method of artificial intelligence has its place in business today. It’s a matter of knowing when to use a fork or a spoon.
Now that you understand the tools in your kit, you can get started on building whatever you need for your business.