This repository is for my self-study.
- Machine Learning(Python)
- Chapter2 (Classification Problem)
- Chapter3 (Using scikit-learn)
- Chapter4 (Data Preprocessing)
- Chapter5 (Dimensionality Reduction)
- Chapter6 (Model Evaluation and Hyperparameter Tuning)
- Chapter7 (Ensemble Method)
- Chapter10 (Regression Analysis)
- Chapter11 (Clustering Analysis)
- Chapter12 (Multilayer Artificial Neural Network)
- Chapter13 (Basics of TensorFlow)
- Chapter14 (Mechanism of TensorFlow)
- Deep Learning(Python)
- Chapter3 (Neural Network)
- Chapter4
- Chapter5
- Chapter6
- Chapter7
- GAN
- Keras
- CNN with Keras
- Industrial Organization(Python, R, Matlab)
- Codes which are used in Grad Empirical IO class (2019, S1S2)
- wald_friedman
- uncertainty_traps
- repeated game
- My master's thesis
- Supermodularity and Equilibrium in Games with Peer Effects and Endogenous Network Formation
- Notes on Julia
- Basics(Studying_Julia.ipynb)
- Matrix(Julia_matrix.ipynb)
- Text mining(aozora.py, abe_network.py)
- Network Simulation(nx_ba_1.py)
- Algorithm for "Coordination on Networks" (network_algorithm.ipynb)
- Algorithm for "Financial Networks and Contagion" (Financial_Network.ipynb)
- Note on LMSR(CostFunction.ipynb)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent