Self-Study

This repository is for my self-study.

Contents

machine-learning

• 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

• Deep Learning(Python)
  • Chapter3 (Neural Network)
  • Chapter4
  • Chapter5
  • Chapter6
  • Chapter7
• GAN
• Keras
• CNN with Keras

Industrial-Organization

• Industrial Organization(Python, R, Matlab)
  • Codes which are used in Grad Empirical IO class (2019, S1S2)

oyamasemi

• wald_friedman
• uncertainty_traps
• repeated game

endo_net

• My master's thesis
  • Supermodularity and Equilibrium in Games with Peer Effects and Endogenous Network Formation

Others

• 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)