Learning Word Embedding
Word embedding is a dense representation of words in the form of numeric vectors. It can be learned using a variety of language models. The word embedding representation is able to reveal many hidden relationships between words. For example, vector(“cat”) - vector(“kitten”) is similar to vector(“dog”) - vector(“puppy”). This post introduces several models for learning word embedding and how their loss functions are designed for the purpose.
Anatomize Deep Learning with Information Theory
This post is a summary of Prof Naftali Tishby’s recent talk on “Information Theory in Deep Learning”. It presented how to apply the information theory to study the growth and transformation of deep neural networks during training.
From GAN to WGAN
This post explains the maths behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs’ training by adopting a smooth metric for measuring the distance between two probability distributions.
How to Explain the Prediction of a Machine Learning Model?
This post reviews some research in model interpretability, covering two aspects: (i) interpretable models with model-specific interpretation methods and (ii) approaches of explaining black-box models. I included an open discussion on explainable artificial intelligence at the end.
Predict Stock Prices Using RNN: Part 2
This post is a continued tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. Part 2 attempts to predict prices of multiple stocks using embeddings. The full working code is available in github.com/lilianweng/stock-rnn.
Predict Stock Prices Using RNN: Part 1
This post is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. Part 1 focuses on the prediction of S&P 500 index. The full working code is available in github.com/lilianweng/stock-rnn.
An Overview of Deep Learning for Curious People
Starting earlier this year, I grew a strong curiosity of deep learning and spent some time reading about this field. To document what I’ve learned and to provide some interesting pointers to people with similar interests, I wrote this overview of deep learning models and their applications.