Untitled

Related lectures (27)

Page 2 of 3

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Covers the basics of image processing using Fiji software, focusing on managing files and applying filters.

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Discusses challenges and advancements in Transformers, pretraining models, and subword tokenization in NLP.