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lec4_2: introduction to PyTorch for macroeconomist
Introduction 1-1
lec5_2: introduction to deep learning
lec4_1: introduction to python for macroeconomist
lec5_1: introduction to Machine learning
lec11-2: machine learning and value function
lec10-2: Computation of Aiyagari Model
第二讲9.14
lec6-2: MPI parellel computing
lec10-1: aiyagari incomplete market
lec11-1: machine learning and FOC
Lec_1_1: Intro
lec7_1: Linearization
lec13-1: dynamic model w/ endogenous state space
lec12-2: generalized markov equilibrium
Lec_4_4: Pytorch optimization
lec_2_2: numerical interpolation
lec7_2: Perturbation
lec12-1: simple markov equilibrium
Lec_1_2: Intro
lec13-2: reinforcenment learning for model w/ endogenous state space
Lec_2_4: Differentiation and interpolation
lec3_1: value function based numerical method
lec8_1: Projection
lec9: calibration and estimation
Lec_2_2: Optimization derivative based
Lec_7_2: Curse of dimensionality in quant macro
Lec_5_2: Perturbation
Lec_3_3: Deep neural network
Lec_2_1: numerical optimization
lec3_2: euler equation based numerical method
Lec_6_1: Projection with polynomials
Lec_2_1: Floating points
lec6-1: high performance computing basic
Lec_7_4: Optimal growth model via Belmanl equation and ML
Lec_7_3: Optimal growth model via Euler equation and ML
lec8_2: finite element
Lec_4_1: Python variables
Lec_5_1: Linearization
Lec_2_3: Optimization derivative free