Mit 6.S191: Recurrent Neural Networks, Transformers, And Attention
Recurrent Neural Networks (RNNs) have been widely used for sequence modeling in various applications ranging from speech recognition to image captioning. However, RNNs suffer from challenges such as vanishing gradients, which hampers their ability to handle long-term dependencies in the sequence. This led to the development of the Transformer architecture, which uses self-attention mechanisms to address the challenges of RNNs.
The Transformer architecture allows for parallel processing and long-range dependencies by weighing the importance of each token in a sequence. This has led to significant improvements in language-related tasks and has become a critical tool for sequence modeling.
Sequence modeling is crucial for handling and learning from sequential data in real-world applications. However, RNNs and the Transformer architecture require careful design and training to handle the challenges of variable length sequences, dependencies, and order. Despite these challenges, these models are powerful tools that continue to drive advancements in the field of sequence modeling.
Recurrent Neural Networks (RNNs) have been a popular choice for modeling sequential data. They use a feedback mechanism to incorporate information from previous time steps, enabling them to capture dependencies and patterns in time series data. However, they are not without their challenges. One of the most significant hurdles is their inability to handle long-range dependencies, resulting in the vanishing gradient problem. This problem arises because the gradient becomes smaller and smaller as it propagates back in time, making it difficult for the network to learn from distant time steps.
