## 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

###### State :

- [
**Finished**] chapter1 : Introduction - [
**Finished**] chapter2 : Linear Algebra - [
**Finished**] chapter3 : Probability and Information Theory - [
**Finished**] chapter4 : Numerical Computation - [
**Finished**] chapter5 : Machine Learning Basics - [
**Finished**] chapter6 : Deep Feedforward Networks - [
**Finished**] chapter7 : Regularization for Deep Learning - [
**Finished**] chapter8 : Optimization for Training Deep Models

##### 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

- SMASH: One-Shot Model Architecture Search through HyperNetworks
- Explanation of deep nerual network
- Structural Deep Embedding for Hyper-Networks
- Class Imbalance, Redux
- Sampling Matters in Deep Embedding Learning
- Training Very Deep Networks

#### 2.2 Generative model

#### 2.3 Knowledge graph

- Learning Structural Node Embeddings via Diffusion Wavelets
- A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
- On Inductive Abilities of Latent Factor Models for Relational Learning
- A Review of Relational Machine Learning for Knowledge Graphs
- Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

#### 2.4 Representation learning

- Hierarchical Density Order Embeddings
- Efficient Estimation of Word Representations in Vector Space
- Neural Word Embedding as Implicit Matrix Factorization
- Dependency-Based Word Embeddings
- Deep contextualized word representations
- Improved Word Representation Learning with Sememes
- Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

#### 2.5 Application of graph neural network(GNN) in NLP

- Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
- Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
- Learning Deep Generative Models of Graphs
- Neural Relational Inference for Interacting Systems

#### 2.6 Theory about graph neural network

#### 2.7 Research on language characteristics

#### 2.8 Relation extraction

- Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
- Neural Relation Extraction with Selective Attention over Instances
- Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
- CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
- Learning with Noise: Enhance Distantly Supervised Relation Extractionwith Dynamic Transition Matrix
- Deep Residual Learning for Weakly-Supervised Relation Extraction
- Cross-Sentence N-ary Relation Extraction with Graph LSTM
- Distant Supervision for Relation Extraction beyond the Sentence

#### 2.9 Casuality inference

#### 2.10 Dialogue system

- Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
- Neural Responding Machine for Short-Text Conversation

#### 2.11 Coreference resolution

- Multi-Task Identification of Entities, Relations, and Coreference
- End-to-end Neural Coreference Resolution

#### 2.12 Machine translation

#### 2.13 Machine reading

- Reinforced Mnemonic Reader for Machine Reading Comprehension
- Teaching Machines to Read and Comprehend

#### 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