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Jun 29, 2020 · What is a Transformer? The Transformer in NLP is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. It relies entirely on self-attention to compute representations of its input and output WITHOUT using sequence-aligned RNNs or convolution. 🤯.
Dec 10, 2023 · Learn about the transformer architecture, a neural network for performing machine learning tasks, especially NLP. Understand the need, working and applications of transformers, such as machine translation, text summarization and speech recognition.
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Learn how Transformers work as language models for various NLP tasks, from translation to summarization. Discover the different types of Transformers, their self-supervised pretraining, and their transfer learning process.
Jun 19, 2019 · Learn how Transformers work in NLP, how they are related to sequence-to-sequence models and attention, and how they enable Google's BERT. Explore the advantages and challenges of Transformers and their applications in various NLP tasks.
A transformer model is a type of deep learning model that was introduced in 2017. These models have quickly become fundamental in natural language processing (NLP), and have been applied to a wide range of tasks in machine learning and artificial intelligence.
Apr 30, 2020 · Learn how transformers work with an example of a conversational chatbot. The post explains the attention mechanism, positional encoding, encoder and decoder layers, and multi-headed attention in detail.
While Transformers are “newcomers” in NLP, they were developed on top of several ideas, the origins of which can be traced back to earlier work, such as word embedding (Bengio et al., 2003; Mikolov et al., 2013) and attention mechanisms (Bahdanau et al., 2014;
