AI (artificial intelligence) has been one of the buzzwords in the recent years and, it looks like it is going to continue been like that for, at least, a few more. Leaving on a side terminologies, the truth is that this kind of technologies offer great opportunities. Lately, there are two terms that we can listen quite often and almost everywhere. These two terms, related with AI, are:
- Machine learning
- Deep learning
Both terms are related, in fact, we can say the machine learning involves deep learning. Both technologies refer systems that can learn by themselves, the difference is the way they learn. As a quick explanation, we can say that deep leaning is more complex, more sophisticated and more autonomous. Once the deep leaning system is implemented the need for human intervention is minimal.
Machine learning
The main characteristic that differentiate these systems from other less advanced is the ability to learn by themselves. In this way, the system algorithm receives a set of rules to apply to the data but, the special thing about this kind of system is they can adapt these rules or develop new ones to increase the successful rate.
For example, let’s say we write a system to identify cat pictures (Internet loves cat pictures, we know). We can ask the system to identify some patterns: four legs, hair, nose, ears, tail, two eyes… All characteristics that usually cats have. After that, we can train the algorithm with a training set pointing to the system if we can find a cat or not. With this action we allow the system to create or adapt their own rules to make easier the task when new and unknown pictures will be given.
Deep learning
The difference between machine learning and deep learning is that the second one takes the learning part to a more advanced level. In this case, the system has layers or neuronal units trying to imitate the brain’s behavior.
In deep learning, each layer process the information and return a result as a percentage. For example, this picture has a 87% change to be a cat and a 13% change to not. The next layer analyzing the image will take this value and it will combine it with its own value. With this, the percentage will vary and, this new value will be sent to the next layer to perform a similar process. This process will continue layer after layer.
All these consecutive analysis performed by the different layers reduce the error rate and increase the number of correct conclusions. To train the system we will use again a training set and, specially in this case, the bigger, the better.
Binary Coders 