Learn the key features of an effective NLP architecture for AI applications, such as data preprocessing, embedding layer, encoder-decoder structure, pre-trained models, and evaluation metrics.

This Project is based on multilingual Translation by using the Transformer with an encoder-decoder architecture along with the multi-head self-attention layers with the positional encoding and ...

This comprehensive guide delves into decoder-based Large Language Models (LLMs), exploring their architecture, innovations, and applications in natural language processing. Highlighting the evolution ...

An encoder-decoder architecture is shown in Fig. 3. The NLP task of converting a prompt into a response is more generally termed a sequence-to-sequence (seq2seq) problem, because both the input and ...

Images are essential for communicating ideas, feelings, and narratives in the era of digital media and content consumption. Computers to produce textual data for an image that replaces humans. Image ...

CNNs excel in handling grid-like data such as images, RNNs are unparalleled in their ability to process sequential data, GANs offer remarkable capabilities in generating new data samples, Transformers ...

Neural Machine Translation using LSTMs and Attention mechanism. Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture ...