Hi there, welcome to my blog! 🙂
I want to share my learning journals, notes and programming exercises with you. The topics include data science, statistics, machine learning, deep learning, AI applications, etc.
- Slideshare (3): Unsupervised Transfer Learning Methods
- TensorFlow 08: save and restore a subset of variables
- To copy or not, that is the question: copying mechanism
- What matters: attention mechanism
- What’s next: seq2seq models
- Deep Learning 16: Understanding Capsule Nets
- Working with ROUGE 1.5.5 Evaluation Metric in Python
- Reinforcement Learning (1): Q-Learning basics
- Deep Learning 15: Unsupervised learning in DL? Try Autoencoder!
- Slideshare (2): Machine Learning Recap Slides sharing
- TensorFlow 07: Word Embeddings (2) – Loading Pre-trained Vectors
- Deep Learning 14 : Optimization, an Overview
- Deep Learning 13: Understanding Generative Adversarial Network
- Is your model good enough? Evaluation metrics in Classification and Regression
- Two sample problem(2): kernel function, feature space and reproducing kernel map
- Understanding SVM(2)
- Two sample problem(1): Parzen Windows, Maximum Mean Discrepancy
- NLP 05: From Word2vec to Doc2vec: a simple example with Gensim
- Deep Learning 12: Energy-Based Learning (2)–Regularization & Loss Functions
- Deep Learning 11: Energy-Based Learning (1)–What is EBL?
How to find me
I am a first-year Ph.D. student, working in LILY lab at Yale University. Please leave comments or contact me via irene.li[at]yale.edu