Transformersrepresent a breakthrough in deep learning, especially for natural language processing. They use attention mechanisms to weigh the importance of different input elements. Unlike previous models, transformers process data in parallel, enabling efficient handling of large datasets. Self-attention al...
深度学习框架(Deep Learning Framework)是目前研究人员开发深度神经网络(Deep Neural Network)的主要工具,对深度神经网络的成功应用和快速发展有重要的促进作用。设计深度学习网络是一个“探索性训练”(Exploratory-Training)的过程,通常以构建一个基础网络(Base Graph)为出发点进行训练,根据训练情况的反馈,对该神经网络的...
2020年10月22日,Google团队发表An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,提出了Vision Transformer(ViT),虽然不是第一篇将Transformer应用在视觉任务的论文,但是因为其模型“简单”且效果好,可扩展性强(scalable,模型越大效果越好),成为了transformer在CV领域应用的里程碑著作,也引...
Learn how deep learning relates to machine learning and AI. In Azure Machine Learning, use deep learning models for fraud detection, object detection, and more.
Designing Deep Learning Models Networks from Scratch With a few lines of code, you can create deep learning networks such as CNNs, LSTMs, GANs, and transformers. Speed up training using multiple GPUs, the cloud, or clusters. In training deep learning models, MATLAB uses GPUs (when available...
Learn how deep learning relates to machine learning and AI. In Azure Machine Learning, use deep learning models for fraud detection, object detection, and more.
Learn what deep learning is, what deep learning is used for, and how it works. Get information on how neural networks and BERT NLP works, and their benefits.
Transformers TitleDatasetDescriptionNotebooks Multilabel DistilBERTJigsaw Toxic Comment ChallengeDistilBERT classifier fine-tuning DistilBERT as feature extractorIMDB movie reviewDistilBERT classifier with sklearn random forest and logistic regression DistilBERT as feature extractor usingembetterIMDB movie reviewDis...
深入探究ConvNets vs. Transformers,哪种预训练模型的可迁移性更好?mp.weixin.qq.com/s/lH4o_g319N4Xeq2hi5UQvg 选取了5个在ImageNet上预训练精度接近的网络(2C + 3T),分别迁移到了包含细粒度分类、场景识别(分类、分割和景深估计)、开放领域图片分类(比如医疗数据和艺术风格识别)、人脸识别、年龄估计等...
a neural network architecture that has been at the heart of language models such as OpenAI’s GPT-3 and Google’s Meena. One of the benefits of Transformers is their capability to learn without the need for labeled data. Transformers can develop representations through unsupervised learning, and...