Please check my notes for the Spring Semester 2020:
Topics covered:
- Text Generation
- BERT understanding
- Knowledge Distillation
- NLP Applications
- EHR+Translation
Author: Irene
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 deploy the deep learning inference service online for any applications. PaddleServing has the following four key features:
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.
Slideshare (8): Graph Neural Network Paper reading notes
The pdf contains some notes on several papers, some of which only have 1-2 slide pages. Again, red fonts are my thoughts and insights. I wish my notes can help readers to better understand the new concepts and get inspired.
Click to view:
Slideshare (7): NeurIPS 2019 Notes on NLP and GCNs
Expanded AAN.how: Learning NLP Made Much Easier!
More resources!
We have almost doubled the number of manually collected resources since our previous release, now totaling over 13,000.
Slideshare (6): Cross-lingual Paper reading notes
During this summer, I did a project on cross-lingual NLP tasks. Recently I was working my notes and I organized them into a better format. I would like to share some of the notes with the readers who might be interested in this topic.
Papers covered:
- A Robust Abstractive System for Cross-Lingual Summarization
- MASS: Masked Sequence to Sequence Pre-training for Language Generation
- Cross-lingual Language Model Pretraining
- Unsupervised Multilingual Word Embeddings
- Context-Aware Cross-Lingual Mapping
- Zero-Shot Cross-Lingual Opinion Target Extraction
- Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
- Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Slideshare (5): Graph Neural Networks
A quick introduction to Graph Neural Networks:
List of papers covered:
Semi-Supervised Classification with Graph Convolutional Networks
Graph Attention Networks
Variational Graph Auto-Encoders
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
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 organized them as one huge PDF (150+ pages).
A very brief outline:
- Sorting
- Data structure + popular questions
- Machine Learning
- SoftDev interview questions
The material covers some screenshots from other people’s lectures and books. [Some slide pages are not in English! I am too lazy to translate them..]
I went through this PDF each time before there is an interview, in the case to answer questions like “what is knn”. I hope you may find the material useful. Download link:
Recently, I am working on a new version by adding more deep learning basics.
New items need to be updated: Merge sort; Sorting code in Python; Boyer-Moore Vote Algorithm.
