Posts

  • 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.