Multi-Modal learning refers to AI models that learn from multiple sources of data, such as text, images, and audio. By incorporating information from multiple modalities, these models can capture richer and more comprehensive representations. Multimodal Learning finds applications in areas like sentiment analysis, image captioning, and video understanding.
ULMFiT is a technique in Natural Language Processing (NLP) that enables transfer learning for NLP tasks. It involves pretraining a language model on a…