Auto-classification Computer Vision Natural Language Processing 
Marktechpost
JULY 31, 2024
Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. It covers various aspects, from using larger datasets to preventing overfitting and moving beyond binary classification.
Marktechpost
MAY 28, 2024
Recent advancements in deep learning offer a transformative approach by enabling end-to-end learning models that can directly process raw biomedical data. Deep Learning in Medical Imaging: Deep learning, particularly through CNNs, has significantly advanced computer vision in medical imaging.
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Marktechpost
DECEMBER 16, 2023
A custom-trained natural language processing (NLP) algorithm, X-Raydar-NLP, labeled the chest X-rays using a taxonomy of 37 findings extracted from the reports. The X-Raydar achieved a mean AUC of 0.919 on the auto-labeled set, 0.864 on the consensus set, and 0.842 on the MIMIC-CXR test.
Towards AI
JULY 23, 2023
In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. The authors introduced the idea of transfer learning in the natural language processing, understanding, and inference world.
Pragnakalp
OCTOBER 6, 2024
Deploying Models with AWS SageMaker for HuggingFace Models Harnessing the Power of Pre-trained Models Hugging Face has become a go-to platform for accessing a vast repository of pre-trained machine learning models, covering tasks like natural language processing, computer vision, and more.
AWS Machine Learning Blog
OCTOBER 6, 2023
Background of multimodality models Machine learning (ML) models have achieved significant advancements in fields like natural language processing (NLP) and computer vision, where models can exhibit human-like performance in analyzing and generating content from a single source of data.
Towards AI
FEBRUARY 13, 2023
Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. The NLP tasks we’ll cover are text classification, named entity recognition, question answering, and text generation. The pipeline we’re going to talk about now is zero-hit classification.
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