Please check my notes for an updated version about Transfer Learning with NLP tasks:
Irene_TransferLearning_2020Spring
A brief outline:
Also, visit my A brief Introduction on Transfer Learning notes from 2019 Spring.
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:
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 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
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:
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.
Please check my notes for Transfer Learning introduction!
A brief introduction on unsupervised transfer learning methods.
The presentation focused on unsupervised transfer learning methods, introducing feature-based and model-based strategies and few recent papers from ICML, ACL.
Unsupervised Transfer Learning
Comments are welcomed!
Few friends with me did some works together since last October. All of us were looking for jobs in machine learning or deep learning. We all agreed that we need to review some interesting algorithms together. We had a draft of machine learning algorithms (part 1) during this new year:
Click here for a full version: mlrecap.
Also, we are working on part 2; there are some advanced algorithms which you can see from our outline. It is expected to finish around this June.
These slides are suitable for people to review old things. Some details are not included, so do not suggest readers learn some concepts from our slides. If you find mistakes, please leave comments. If you are interested in some particular algorithms, leave comments and we will consider updating our part 2 outline.
