In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example?

From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clustering the data without any labels given. But, the problem is I always …

2018年11月13日 · Supervised learning can use "batch" learning, in which the model must be trained on all the training data at once, then deployed, or "online" learning, which can learn …

2014年3月15日 · I also know that classification is a type of supervised learning. But what make me confused is: linear regression (line fitting) is a type of regression? if so , why its data is …

5 While reading about Supervised Learning, Unsupervised Learning, Reinforcement Learning I came across a question as below and got confused. Please help me in identifying in below …

2013年10月4日 · Semi-supervised learning attempts to combine unlabeled and labeled data (or, more generally, sets of unlabeled data where only some data points have labels) into integrated …

2022年2月17日 · The pre-training phase of models like GPT is considered unsupervised because it learns from unlabeled data. In contrast, supervised learning relies on labeled data, which …

Supervised Learning is a way of "teaching" the classifier, using labeled data. Unsupervised Learning lets the classifier "learn by itself", for example, using clustering.

I see, so, is it like indirect system? I mean, in this example of music recommender, how will supervised learning look like, will it be a system that directly checks music types instead of …

2022年4月9日 · Metric learning is an umbrella term that encompasses several methods for learning embeddings, including contrastive learning and triplet loss-based methods. These …