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2016-12-01-Chapter1_introduction.md

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2016-12-02-Chapter2_linear_algebra.md

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2016-12-03-Chapter3_probability_and_information_theory.md

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2016-12-04-Chapter4_numerical_computation.md

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2016-12-05-Chapter5_machine_learning_basics.md

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2016-12-06-Chapter6_deep_feedforward_networks.md

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2016-12-07-Chapter7_regularization.md

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2016-12-08-Chapter8_optimization_for_training_deep_models.md

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2016-12-09-Chapter9_convolutional_networks.md

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2016-12-10-Chapter10_sequence_modeling_rnn.md

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2016-12-11-Chapter11_practical_methodology.md

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2016-12-12-Chapter12_applications.md

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2016-12-13-Chapter13_linear_factor_models.md

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2016-12-14-Chapter14_autoencoders.md

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2016-12-15-Chapter15_representation_learning.md

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2016-12-16-Chapter16_structured_probabilistic_modelling.md

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2016-12-17-Chapter17_monte_carlo_methods.md

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2016-12-18-Chapter18_confronting_the_partition_function.md

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2016-12-19-Chapter19_approximate_inference.md

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2016-12-20-Chapter20_deep_generative_models.md

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