These are the notebooks what I have read for quick recall whenever I want to check sone points I have learned.
All IN CHINESE
Notebooks about books and courses I read.
causality models reasoning and inference by Pearl.J
- [Unfinished] chapter 1 : Introduction to Probabilities, Graphs, and Causal Models
- [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
Pattern Recognition and Machine Learning
- [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
Gaussian Processes for Machine Learning
- [Unfinished] chapter2 : Regression
Notebooks of some important papers I have read about NLP and ML, DL
- 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
- 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
- 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
- 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
- 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
- Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
- Neural Responding Machine for Short-Text Conversation
- Multi-Task Identification of Entities, Relations, and Coreference
- End-to-end Neural Coreference Resolution
- Reinforced Mnemonic Reader for Machine Reading Comprehension
- Teaching Machines to Read and Comprehend
- 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
- How to deal with big file
- Introduction about Chainer
- Introduction about Pandas
- Introduction about tensorflow
- Programing tricks in python
- Python mechanism
- Introduction about SQL
- 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