京东 11.11 红包
lec5_2: introduction to deep learning
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lec5_1: introduction to Machine learning
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Introduction 1-2
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lec11-2: machine learning and value function
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Introduction 1-1
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lec4_2: introduction to PyTorch for macroeconomist
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lec7_1: Linearization
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Lec_3_3: Deep neural network
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lec7_2: Perturbation
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lec11-1: machine learning and FOC
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Lec_8_1: Krusell-Smith model via Machine Learning
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Lec_3_1: What is machine learning
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lec8_1: Projection
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Lec_2_1: numerical optimization
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lec9: calibration and estimation
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lec8_2: finite element
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Lec_3_2: How machine learning works, pattern recognition
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Quant Macro w/ Machine Learning 2: Computation Basics
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lec10-1: aiyagari incomplete market
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lec3_2: euler equation based numerical method
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lec_2_2: numerical interpolation
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lec4_1: introduction to python for macroeconomist
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Quant Macro w/ Machine Learning 5: Projection Methods
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lec10-2: Computation of Aiyagari Model
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lec12-1: simple markov equilibrium
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Lec_7_1: Review of Machine learning
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Quant Macro w/ Machine Learning 3: Numerical Solution of Dynamic Models
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Quant Macro w/ Machine Learning 9: Krusell-Smith Model
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Quant Macro w/ Machine Learning 4: Linearization and Perturbation
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Lec_6_1: Projection with polynomials
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lec6-2: MPI parellel computing
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lec13-2: reinforcenment learning for model w/ endogenous state space
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Lec_2_2: Optimization derivative based
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lec6-1: high performance computing basic
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Lec_1_2: Intro
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Lec_4_1: Python variables
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Lec_4_2: Python operations
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Lec_5_2: Perturbation
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Quant Macro w/ Machine Learning 1: Intro
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Lec 3: gain from trade
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Lec_6_2: Smolyak's algorithm