This article reviews recent advances in deep learning for text classification, including convolutional neural networks and recurrent neural networks.
This course covers the basics of text classification using deep learning frameworks such as TensorFlow and PyTorch, and explores applications in sentiment analysis and topic modeling.
The NLTK library provides tools for text classification, including support for deep learning frameworks, and is widely used in natural language processing research.
This research paper compares the performance of different deep learning frameworks, including TensorFlow, PyTorch, and Keras, on text classification tasks.
This article provides a tutorial on text classification using deep learning frameworks, including code examples and explanations of key concepts such as word embeddings and recurrent neural networks.
H2O.ai's Driverless AI platform provides automated machine learning for text classification, including support for deep learning frameworks and natural language processing techniques.
This tutorial covers the use of transformer models, such as BERT and RoBERTa, for text classification tasks, and provides examples of how to implement these models using the Hugging Face Transformers library.
This course covers the application of deep learning techniques to natural language processing tasks, including text classification, sentiment analysis, and machine translation, and is taught by leading researchers in the field.