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Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, [24] generative pre-training, ELMo, [25] and ULMFit. [26] Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus .
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Higher-order thinking, also known as higher order thinking skills (HOTS), [1] is a concept applied in relation to education reform and based on learning taxonomies (such as American psychologist Benjamin Bloom's taxonomy). The idea is that some types of learning require more cognitive processing than others, but also have more generalized benefits.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Bahasa Indonesia; עברית ... Pages in category "Deep learning" The following 48 pages are in this category, out of 48 total. This list may not reflect recent ...
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.