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Introductory book acknowledgments: introduction. Applied math and machine learning basics: linear algebra; probability and information theory; numerical computation; machine learning basiccs. Deep net works: modern practices: deep feedforward networks; regularization for deep learning; optimization for training deep models; convolutional networks; sequence modeling: recurrent and recursive nét; practical methodology; applications. Deep learning research: linear factor models; autoencoders; representation learning; structured probabilistic models for deep learning; monte carlo methods; confronting the partition; approximate inference; deep generative models.