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Module 1: Introduction & General Overview
Module 2a: Pytorch Tensors
Module 2b: Automatic Differentiation
Module 3: Loss functions for classification
Module 4: Optimization for Deep Learning
Module 5: Stacking layers
Module 6: Convolutional Neural Network
Module 7: Dataloading
Module 8a: Embedding layers
Module 8b: Collaborative Filtering
Modules 9: Autoencoders
Module 10: Generative Adversarial Networks
Module 11a: Recurrent Neural Networks theory
Module 11b: Recurrent Neural Networks practice