Billy Ian's Short Leisure-time Wander

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Trace of My Study on Machine Learning

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This blog will record the timeline, resources, projects along the way of my study on machine learning since 2015. And I will keep updating this blog.


Statistical Machine Learning

Probabilistic Graphical Models:

Deep Learning

  • Neural Network and Deep Learning: Nice online tutorial for beginners in deep learning (Completed in Sep, 2016).
  • Coursera: Neural Networks for Machine Learning: Excellent course for beginners in neural networks. (Certificate)
  • Stanford CS224d: Deep Learning for Natural Language Processing (Completed in Apr, 2017).
  • Stanford CS231n: Convolutional Neural Networks for Visual Recognition.
  • Stanford CS236: Deep Generative Models. (Completed in Dec, 2018)
  • Deep Learning Book: First and second part are great to read and need to be understood well. (First Pass in Oct, 2018)
  • TensorFlow: Large amount of high-quality tutorials and well-coded recent advances in DL. Most applicable scenario: research.
  • PyTorch: Use dynamic graph instead of static graph in TensorFlow. Relatively easy to use compared to TensorFlow. Most applicable scenario: research.
  • Mxnet: High performance. Most applicable scenario: industry.
  • Keras: High-level, easy to use. Most applicable scenario: competitions like Kaggle.

Note: I’m comfortable with coding in both TensorFlow and PyTorch and use Keras from time to time. I don’t know much about Mxnet and rarely use it, but I’m amazed by their group members.

Large Scale Machine Learning

Reinforcement Learning

ML Competitions