Let’s say you have the picture of a cute puppy. You want to classify whether it’s a picture of a puppy, of a cat, of a hippopotamus or of a giraffe.
You could train a fully connected network to solve this task:
Machine learning algorithms try to imitate the pattern between two datasets in such a way that they can use one dataset to predict the other. Specifically, supervised machine learning is useful for taking what you know as input and quickly transforming it into what you want to know. On the other hand, unsupervised learning also transforms one dataset into another, but the dataset that it transforms into is not previously known or understood. Unlike supervised learning, there is no “right answer” that you’re trying to get the model to duplicate. …
In this article, we will focus on agents that can deal with feedback that’s simultaneously sequential and evaluative. And even most humans have problems with simultaneously balancing immediate and long-term goals and the gathering and utilization of information.
PhD student in Machine Learning — also interested in Neuroscience and Cognitive Science.