Lec_2_4: Differentiation and interpolation
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Lec_2_1: Floating points
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Lec_3_4: Feed forward and backpropagation
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lec6-2: MPI parellel computing
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lec10-2: Computation of Aiyagari Model
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Lec_2_1: numerical optimization
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Lec_2_2: Optimization derivative based
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Lec_5_2: Perturbation
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Lec_8_1: Krusell-Smith model via Machine Learning
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Lec_7_3: Optimal growth model via Euler equation and ML
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lec10-1: aiyagari incomplete market
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Lec_4_1: Python variables
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lec12-1: simple markov equilibrium
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lec12-2: generalized markov equilibrium
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Lec 18: fiscal policy
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Lec 6: macro overview
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Lec 15: AD
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Lec 21: monetary policy
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Lec 12: MPC, multipler
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Lec_4_2: Python operations
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Lec_6_3: Finite Element Methods
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Lec 13: C function
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Lec_4_4: Pytorch optimization
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Lec 1: principles in micro
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Lec 19: debt
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lec13-1: dynamic model w/ endogenous state space
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Lec_4_3: Pytorch basics
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Lec_7_1: Review of Machine learning
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Lec 3: gain from trade
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Lec 10: labor market
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lec13-2: reinforcenment learning for model w/ endogenous state space
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Lec 17: AD-AS model
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Lec 2: production possibility frontier
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lec6-1: high performance computing basic
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Introduction 1-2
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lec7_2: Perturbation
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Introduction 1-1
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lec11-1: machine learning and FOC
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Lec_7_4: Optimal growth model via Belmanl equation and ML
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Lec_3_1: What is machine learning
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lec3_2: euler equation based numerical method