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Embeddings Unveiled: The Hidden Language of Data Science (with python examples)
- Embeddings solve the problem of traditional approaches to data representation.
- There are various approaches for embedding models like Word2Vec, GloVe, FastText.
- LSA is a widely-used approach that uses Singular Value Decomposition (SVD) for dimensionality reduction.
- Probabilistic Topic Modeling (PTM) is a popular technique that uses generative models to determine the latent topics in a corpus of documents.
- ELMo uses a bidirectional language model (BiLM) to create dynamic and contextualized embeddings for words.
- BERT is a pretrained language model designed to understand the context of a word in relation to the words surrounding it.
- GPT is primarily designed for text generation tasks and uses the decoder part of the Transformer architecture.
- T5 is a model that treats every NLP task as a text-to-text problem.
- GPT-3 is one of the largest and most influential foundation models in NLP and is autoregressive.
- PaLM is built on the Pathways architecture, which enables it to learn from a more diverse set of tasks.
- LaMDA is specially designed for dialogue generation and training to improve the quality of conversation in open-ended dialogues.
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