308 An introduction to language models with focus on GPT

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Video 308: An introduction to language models, With a special focus on GPT • Language models are the foundation of many natural language processing (NLP) tasks. • They help machines understand and generate human language by predicting the likelihood of a sequence of words. • Over the years, advances in algorithms and computational power have driven progress in language modeling, enabling breakthroughs in NLP applications. • LSTM networks, introduced by Hochreiter and Schmidhuber in 1997, are a type of recurrent neural network (RNN) designed to handle long-term dependencies. • Traditional RNNs struggled with the vanishing gradient problem, making it difficult to capture context over longer sequences. • LSTMs addressed this issue with their unique gating mechanisms, which enabled them to retain information for more extended periods, paving the way for improved language modeling. • (Watch my video on this topic: • 167 - Text prediction using LSTM (Eng... ) • The transformer architecture, introduced by Vaswani et al. in 2017, revolutionized NLP by utilizing self-attention mechanisms and parallel processing. • The Transformer model is based on the encoder-decoder architecture. • Encoder: Processes input sequence, generating contextualized representations of each token. • Decoder: Generates output sequence step by step, using encoder's output as context for informed predictions. • Self-attention allows the model to weigh the importance of different words in a sequence, enabling better context understanding. • Parallel processing overcomes the sequential processing limitations of RNNs, leading to faster training and improved performance on various NLP tasks. • BERT (Bidirectional Encoder Representations from Transformers) is well-suited for tasks that require understanding the context of both preceding and following tokens. Some good applications for BERT include: • Sentiment analysis • Named entity recognition • Question-answering systems • Text classification • Semantic role labeling • GPT (Generative Pre-trained Transformer) is primarily designed for text generation tasks, and it is a unidirectional model, meaning it processes text in a left-to-right fashion. Some good applications for GPT include: • Text completion • Machine translation • Summarization • Chatbots and conversational AI • Creative writing assistance • GPT, developed by OpenAI, is a transformer-based model with a focus on decoding and adaptability. • GPT models, particularly GPT-3, have demonstrated impressive capabilities in zero-shot and few-shot learning, where they can learn new tasks with minimal or no examples. • While GPT excels at text generation and learning from examples without fine-tuning, it is important to consider its limitations, such as the size and computational requirements of the model, when evaluating its practical applications.

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