Deep Learning
Encoder-decoder models are a powerful tool for sequence-to-sequence prediction tasks. They have been used to achieve state-of-the-art results on a wide range of tasks, including machine translation, text summarization, and question answering.
Deep Learning
LLMs are a powerful tool that can be used for a variety of tasks, including code generation, translation, and question answering. As LLMs continue to improve, they are likely to play an even greater role in our lives.
Deep Learning
Model quantization is a technique to reduce the size and computational complexity of deep learning models without sacrificing too much accuracy. It works by converting the model's weights and activations from high-precision floating-point numbers to lower-precision integer or fixed-point numbers.
AI
Attention mechanisms are a powerful tool that can be used to improve the performance, interpretability, and efficiency of deep learning models. They are particularly useful for tasks where the input data is large and complex, such as machine translation, image captioning, and speech recognition.
AI
Transformers are like toy train sets for language. They read the text and try to understand what it's about. They then generate text based on their understanding.