transcripts/transcript_to_module.json at main · dataflowr/transcripts · GitHub

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{
  "dataflowr_module1.txt": {"module_id": "1", "module_title": "Introduction & General Overview"},
  "dataflowr_module2a.txt": {"module_id": "2a", "module_title": "PyTorch Tensors"},
  "dataflowr_module2b.txt": {"module_id": "2b", "module_title": "Automatic Differentiation"},
  "dataflowr_module3.txt": {"module_id": "3", "module_title": "Loss Functions for Classification"},
  "dataflowr_module4.txt": {"module_id": "4", "module_title": "Optimization for Deep Learning"},
  "dataflowr_module5.txt": {"module_id": "5", "module_title": "Stacking Layers"},
  "dataflowr_module6.txt": {"module_id": "6", "module_title": "Convolutional Neural Networks"},
  "dataflowr_module7_8a.txt": {"module_id": "7+8a", "module_title": "Dataloading & Embedding Layers"},
  "dataflowr_module8b.txt": {"module_id": "8b", "module_title": "Collaborative Filtering"},
  "dataflowr_module9a.txt": {"module_id": "9a", "module_title": "Autoencoders"},
  "dataflowr_module10.txt": {"module_id": "10", "module_title": "Generative Adversarial Networks"},
  "dataflowr_module11a.txt": {"module_id": "11a", "module_title": "Recurrent Neural Networks (Theory)"},
  "dataflowr_module11b.txt": {"module_id": "11b", "module_title": "Recurrent Neural Networks (Practice)"},
  "dataflowr_module11c.txt": {"module_id": "11c", "module_title": "Batches with Sequences in PyTorch"},
  "dataflowr_module13.txt": {"module_id": "13", "module_title": "Siamese Networks and Representation Learning"},
  "dataflowr_module14a.txt": {"module_id": "14a", "module_title": "The Benefits of Depth"},
  "dataflowr_module14b.txt": {"module_id": "14b", "module_title": "The Problems with Depth"},
  "dataflowr_module15.txt": {"module_id": "15", "module_title": "Dropout"},
  "dataflowr_module16.txt": {"module_id": "16", "module_title": "Batch Normalization"},
  "dataflowr_module17.txt": {"module_id": "17", "module_title": "ResNets"},
  "dataflowr_practical1.txt": {"module_id": "1p", "module_title": "Practical: More Dogs and Cats"},
  "dataflowr_practical2.txt": {"module_id": "2bp", "module_title": "Practical: Backpropagation"},
  "dataflowr_practical3.txt": {"module_id": "4p", "module_title": "Practical: Optimizers"},
  "dataflowr_practical4.txt": {"module_id": "6p", "module_title": "Practical: First CNN"},
  "dataflowr_practical5a.txt": {"module_id": "8bp1", "module_title": "Practical: Collaborative Filtering (Part 1)"},
  "dataflowr_practical5b.txt": {"module_id": "8bp2", "module_title": "Practical: Collaborative Filtering (Part 2)"},
  "dataflowr_practical_autoencoders.txt": {"module_id": "9ap", "module_title": "Practical: Autoencoders"},
  "dataflowr_practical_gan.txt": {"module_id": "10p", "module_title": "Practical: GANs"},
  "dataflowr_practical7.txt": {"module_id": "11bp", "module_title": "Practical: Predicting Engine Failure with RNN"},
  "dataflowr_transfer_learning.txt": {"module_id": "bonus_tl", "module_title": "Bonus: Transfer Learning"},
  "dataflowr_bonus_julia_autodiff.txt": {"module_id": "bonus_julia", "module_title": "Bonus: Automatic Differentiation with Julia"},
  "dataflowr_bonus_privacy_ml.txt": {"module_id": "bonus_privacy", "module_title": "Bonus: Privacy Preserving Machine Learning"},
  "dataflowr_bonus_graphs1_node_embedding.txt": {"module_id": "graph0a", "module_title": "Bonus: Deep Learning on Graphs — Node Embedding"},
  "dataflowr_bonus_graphs2_signal_processing.txt": {"module_id": "graph0b", "module_title": "Bonus: Deep Learning on Graphs — Signal Processing"},
  "dataflowr_bonus_graphs3_graph_embedding.txt": {"module_id": "graph0c", "module_title": "Bonus: Deep Learning on Graphs — Graph Embedding"}
}