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:
Continue reading “Slideshare (8): Graph Neural Network Paper reading notes”
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.
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 model only sees the trained samples, it will eventually lose the ability to “create” more! So we add some “noises” to the parameters by forcing the parameters to adapt to a known distribution.
Continue reading “Understanding Variational Graph Auto-Encoders”
Graph Convolution Networks (GCNs) [0] deal with graphs where the data form with a graph structure. A typical graph is represented as G(V, E), where V is the collection of all the nodes and Eis the collection of all the edges.
Continue reading “Understanding Graph Convolutional Networks”
Please check my notes for Transfer Learning introduction!
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