One-shot learning is the task of learning to recognize a new object after only a single exposure to that object. This is in contrast to most machine learning tasks, which require multiple examples of each object in order to learn to recognize it. One-shot learning is difficult because it requires the learner to generalize from…
Tag: transfer-learning
Lack of labeled data
The lack of labeled data limits the current state of AI. This is a problem because most data is unstructured and not labeled. We need to find new and creative ways to label data to solve this problem. One way to label data is through active learning. Active learning is a process where the user…
Transfer learning
Transfer learning is a technique that can be used to speed up the training of machine learning models and improve their accuracy. The current state of AI technology requires that data be manually labeled in order for machines to learn from it. This process is both time-consuming and expensive. Additionally, it limits the types of…
Hidden Markov models that are difficult to train
There are a number of different ways to go about using AI to learn the patterns in hidden Markov models. One approach is to use a technique called deep learning. Deep learning is a type of machine learning that is particularly well suited to learning patterns in data. Deep learning is a subset of machine…
Neural networks that overfit the training data
Overfitting is a common problem in machine learning and artificial intelligence. Neural networks are especially prone to overfitting because they are so flexible and can learn complex patterns. Overfitting means that the neural network has learned the training data too well and does not generalize well to new data. This can be a problem because…