What are the key differences between BERT's bidirectional training approach and GPT's autoregressive model, and how do these differences impact their performance on various NLP tasks?
BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are two prominent models in the realm of natural language processing (NLP) that have significantly advanced the capabilities of language understanding and generation. Despite sharing some underlying principles, such as the use of the Transformer architecture, these models exhibit fundamental differences in their training
How can we implement LSTM in TensorFlow to analyze a sentence both forwards and backwards?
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that is widely used in natural language processing (NLP) tasks. LSTM networks are capable of capturing long-term dependencies in sequential data, making them suitable for analyzing sentences both forwards and backwards. In this answer, we will discuss how to implement an LSTM
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Natural Language Processing with TensorFlow, Long short-term memory for NLP, Examination review