Showing 1-20 of 29 projects
A tutorial on deep learning by renowned professor Li Hongy, covering a wide range of AI and machine learning topics.
A comprehensive repository covering papers, codes, datasets, tutorials, and applications for transfer learning, domain adaptation, and more.
Fast and accurate machine learning framework that can build models in just 3 lines of Python code.
A curated list of resources related to domain adaptation, a technique used to improve AI model performance on new datasets.
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
A high-performance face recognition library for developers built on PaddlePaddle and PyTorch.
The next generation deep reinforcement learning toolkit for AI researchers and developers.
A tutorial on transfer learning, a fundamental technique in machine learning and AI.
A collection of Jupyter Notebook tutorials on solving real-world problems with machine learning and deep learning using PyTorch.
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification.
EasyNLP is an easy-to-use NLP toolkit for tasks like text classification, retrieval, and generation.
SparseML provides a library for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models.
An integrated federated learning library for research and production use cases.
A curated list of resources for transfer learning and domain adaptation research and applications.
A state-of-the-art conversational AI library using transfer learning and GPT-2 models.
A powerful NLP library for transfer learning and building question answering systems.
jiant is an NLP toolkit that provides pre-trained BERT models and tools for multi-task learning and transfer learning.
Measuring Massive Multitask Language Understanding with GPT-3 and few-shot learning techniques.
Official repository for the 'Big Transfer (BiT): General Visual Representation Learning' paper, focused on transfer learning for computer vision.
A library for self-supervised learning on graphs, providing contrastive, generative, and predictive pretext tasks.
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