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.
A quick introduction to Graph Neural Networks: 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 organizedContinue reading “Prepare for the Interviews!”
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”
Hi! In the following posts, I will introduce Q-Learning, the first part to learn if you want to pick up reinforcement learning. But before that, let us shed light on some fundamental concepts in reinforcement learning (RL). Kindergarten Example Q-Learning works in this way: do an action and get reward and observation from the environment,Continue reading “Reinforcement Learning (1): Q-Learning basics”
There are unsupervised learning models in multiple-level learning methods, for example, RBMs and Autoencoder. In brief, Autoencoder is trying to find a way to reconstruct the original inputs — another way to represent itself. In addition, it is useful for dimensionality reduction. For example, say there is a 32 * 32 -sized image, it isContinue reading “Deep Learning 15: Unsupervised learning in DL? Try Autoencoder!”
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 fullContinue reading “Slideshare (2): Machine Learning Recap Slides sharing”
As a cardinal part of deep learning or machine learning, optimization has long been a mathematical problem for researchers. Why we need optimization? Remember you have a loss function for linear regression, and then you would need to find the optimum of the function to minimize the square error, for example. You might also veryContinue reading “Deep Learning 14 : Optimization, an Overview”
Proposed in 2014, the interesting Generative Adversarial Network (GAN) has now many variants. You might not surprised that the relevant papers are more like statistics research. When a model was proposed, the evaluations would be based on some fundamental probability distributions, where generalized applications start.
Find Two sample problem (1) here. We will take a look at RHKS (Reproducing Hilbert Kernel Space ) in this post. You might think of it a very statistical term but it is amazing because of various applications. You will need to refresh your mind for some linear algebra computations. We start with some basicContinue reading “Two sample problem(2): kernel function, feature space and reproducing kernel map”