Explore the intersection of deep learning and biotechnology, focusing on Word2Vec and its applications in protein structure prediction. Understand the transformation from discrete to continuous space, the challenges of traditional word representation methods, and the implications for computational l

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Explore how cough sounds can aid in COVID-19 classification through autoregressive predictive coding pretraining and spectral data augmentation. Leveraging datasets like DiCOVA and COUGHVID, the goal is to develop a model that can distinguish COVID-19 based on cough type, providing a scalable and re

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Dive into the world of Transformer Neural Networks with insights on their applications in assessment tasks. Explore the evolution of NLP methods and understand why transformers enable more accurate scoring and feedback. Uncover key concepts and processes involved in model pretraining for language ta

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This informative content delves into the evolution and significance of BERT - a prominent model in NLP. It discusses the background history of text representations, introduces BERT and its pretraining tasks, and explores the various aspects and applications of contextualized language models. Delve i

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This project aims to provide a comprehensive medical imaging dataset for two-stage transfer learning, facilitating the evaluation of architectures utilizing this approach. Transfer learning in medical imaging involves adapting pre-trained deep learning models for specific diagnostic tasks, enhancing

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Large amount of parallel speech-text data is not available in most languages, leading to the development of wav2vec for ASR systems. The training process involves self-supervised pretraining and low-resource finetuning. The model architecture includes a multi-layer convolutional feature encoder, qua

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