Lesson 3 In-Class Discussion

Part 1 (2018)
240

Video timelines for Lesson 3

  • 00:00:05 Cool guides & posts made by Fast.ai classmates
    • tmux, summary of lesson 2, learning rate finder, guide to Pytorch, learning rate vs batch size,
    • decoding ResNet architecture, beginner’s forum
  • 00:05:45 Where we go from here
  • 00:08:20 How to complete last week assignement “Dog breeds detection”
  • 00:08:55 How to download data from Kaggle (Kaggle CLI) or anywhere else
  • 00:12:05 Cool tip to download only the files you need: using CulrWget
  • 00:13:35 Dogs vs Cats example
  • 00:17:15 What means “Precompute = True” and “learn.bn_freeze”
  • 00:20:10 Intro & comparison to Keras with TensorFlow
  • 00:30:10 Porting PyTorch fast.ai library to Keras+TensorFlow project
  • 00:32:30 Create a submission to Kaggle
  • 00:39:30 Making an individual prediction on a single file
  • 00:42:15 The theory behind Convolutional Networks, and Otavio Good demo (Word Lens)
  • 00:49:45 ConvNet demo with Excel,
    • filter, Hidden layer, Maxpool, Dense weights, Fully-Connected layer
  • Pause
  • 01:08:30 ConvNet demo with Excel (continued)
    • output, probabilities adding to 1, activation function, Softmax
  • 01:15:30 The mathematics you really need to understand for Deep Learning
    • Exponentiation & Logarithm
  • 01:20:30 Multi-label classification with Amazon Satellite competition
  • 01:33:35 Example of improving a “washed-out” image
  • 01:37:30 Seting different learning rates for different layers
  • 01:38:45 ‘data.resize()’ for speed-up, and ‘metrics=[f2]’ or ‘fbeta_score’ metric
  • 01:45:10 ‘sigmoid’ activation for multi-label
  • 01:47:30 Question on “Training only the last layers, not the initial freeze/frozen ones from ImageNet models”
    • ‘learn.unfreeze()’ advanced discussion
  • 01:56:30 Visualize your model with ‘learn.summary()’, shows ‘OrderedDict()’
  • 01:59:45 Working with Structured Data “Corporacion Favorita Grocery Sales Forecasting”
    • Based on the Rossman Stores competitition
  • 02:05:30 Book: Python for Data Analysis, by Wes McKinney
  • 02:13:30 Split Rossman columns in two types: categorical vs continuous
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