AAN.how: All About NLP —— New 2022 Release: over 24,000 resources and over 7,000 lecture notes!

🎉 We are pleased to release the new, 2022 version of the AAN database with over 24,000 resources and over 7,000 lecture notes. Visit aan.how! 2022 TutorialBank This update includes 19,765 manually collected resources with valid URLs, meta-data, organized by topic. We also release an extra batch of 5,001 resources. These resources have valid URLs butContinue reading “AAN.how: All About NLP —— New 2022 Release: over 24,000 resources and over 7,000 lecture notes!”

Deep Learning 19: Training MLM on any pre-trained BERT models

MLM, masked language modeling, is an important task for trianing a BERT model. In the orignal BERT paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, it is one of the main tasks of how BERT was pre-trained. So if you have your own corpus, it is possible to train MLM on any pre-trainedContinue reading “Deep Learning 19: Training MLM on any pre-trained BERT models”

Slideshare (11): Graph Neural Networks and Applications in NLP

Check out my class talk slides about Graph Neural Networks and their applications in NLP! Covered materials: Semi-Supervised Classification with Graph Convolutional Networks Variational Graph Auto-Encoders Graph Attention Networks Graph Convolutional Networks for Text Classification (AAAI 2019) Heterogeneous Graph Neural Networks for Extractive Document Summarization (ACL 2020) A Graph-based Coarse-to-fine Method for Unsupervised Bilingual LexiconContinue reading “Slideshare (11): Graph Neural Networks and Applications in NLP”

Paddle Serving: model-as-a-service! Triggered by a single command line, deployment finishes in 10 minutes

To bridge the gap between Paddle Serving and PaddlePaddle framework, we release the new service of PaddleServing: Model As A Service (MAAS) online in Github. With the help of the new service, when a PaddlePaddle model is trained, users now can obtain the corresponding inference model at the same time, making it possible to deployContinue reading “Paddle Serving: model-as-a-service! Triggered by a single command line, deployment finishes in 10 minutes”

Slideshare (9): Transfer learning tutorial 2.0

Please check my notes for an updated version about Transfer Learning with NLP tasks: Irene_TransferLearning_2020Spring A brief outline: Transfer Learning with word embedding Pre-BERT times BERT and its variants Understanding, reducing BERT Transfer Learning in the real world   Also, visit my A brief Introduction on Transfer Learning notes from 2019 Spring.

Prepare for the Interviews!

For the past few years after my Master’s, I did many jobs, long term, short term, internship, or full-time. I also had too many interviews, some of them I failed. Together with my friends, we had collected many materials, including basic algorithms, popular questions, basic machine learning knowledge, and deep learning knowledge. Then I organizedContinue reading “Prepare for the Interviews!”

Understanding Variational Graph Auto-Encoders

Variational Auto-Encoders My post about Auto-encoder. For Variational Auto-Encoders (VAE) (from paper Auto-Encoding Variational Bayes), we actually add latent variables to the existing Autoencoders. The main idea is, we want to restrict the parameters from a known distribution. Why we want this? We wish the generative model to provide more “creative” things. If the modelContinue reading “Understanding Variational Graph Auto-Encoders”