My-notebooks

These are the notebooks what I have read for quick recall whenever I want to check sone points I have learned.

All IN CHINESE

1. Books&courses

Notebooks about books and courses I read.

1.1 Casuality

causality models reasoning and inference by Pearl.J

State :
  • [Unfinished] chapter 1 : Introduction to Probabilities, Graphs, and Causal Models

1.2 Deep learning

Deep Learning

State :
1.3 PRML

Pattern Recognition and Machine Learning

State :
  • [Unfinished] chapter1 : Introduction
    • Only 1.5 section
  • [Finished] chapter2 : Probability Distributions
  • [Finished] chapter8 : Graphical Models
  • [Unfinished] chapter9 : Mixture Models and EM
  • [Unfinished] chapter10 : Approximate Inference
1.4 Gaussian Process

Gaussian Processes for Machine Learning

State :
  • [Unfinished] chapter2 : Regression

2. Papers

Notebooks of some important papers I have read about NLP and ML, DL

2.1 Deep learning

2.2 Generative model

2.3 Knowledge graph

2.4 Representation learning

2.5 Application of graph neural network(GNN) in NLP

2.6 Theory about graph neural network

2.7 Research on language characteristics

2.8 Relation extraction

2.9 Casuality inference

2.10 Dialogue system

2.11 Coreference resolution

2.12 Machine translation

2.13 Machine reading

2.14 Entity alignment

  • BIG-ALIGN: Fast Bipartite Graph Alignment
  • Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
  • LinkNBed: Multi-Graph Representation Learning with Entity Linkage
  • A Joint Embedding Method for Entity Alignment of Knowledge Bases
  • Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
  • Iterative Entity Alignment via Joint Knowledge Embeddings
  • Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

3. Topics

3.1 About programing

  • How to deal with big file
  • Introduction about Chainer
  • Introduction about Pandas
  • Introduction about tensorflow
  • Programing tricks in python
  • Python mechanism
  • Introduction about SQL

3.2 About machine learning

  • Bagging, boosting
  • Bayes error
  • Befree
  • Dropout
  • Hessian matrix
  • Imbalance data
  • LSTMs
  • MLE&MAP
  • Regularation
  • Semantics parsing
  • Text similarity
  • posterior&prior
  • Convolution neural network
  • Variational inference
  • NER
  • Inductive bias
  • Lapalance matrix in network thermal conduction
  • Lagrangian
  • Spectral clustering
  • Metrics

My Notebook

My Notebook Info

⭐ Stars 11
🔗 Source Code github.com
🕒 Last Update a year ago
🕒 Created 3 years ago
🐞 Open Issues 0
➗ Star-Issue Ratio Infinity
😎 Author iszhaoxin