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.
- What’s next: seq2seq models (1)
- 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!
- ML 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?
- TensorFlow 06: Word Embeddings (1)
- NLP 04: Log-Linear Models for Tagging Task (Python)
- TensorFlow 05: Understanding Basic Usage
- NLP 03: Finding Mr. Alignment, IBM Translation Model 1
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