Lesson 2 Video Timeline

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Lesson 2 Video

  • 0:09 - Teaching Approach
    • 5:22 - How to Ask For Help (Tips)
    • 7:10 - How to Ask For Help (Example)
    • 8:30 - Class Resources: Wiki
    • 9:55 - Class Resources: Forum
    • 10:25 - Class Resources: Slack
    • 11:20 - Class Survey
  • 17:14 - Solution to Dogs vs Cats Redux Competition
    • 17:30 - Downloading the Data
    • 20:00 - Planning (Overview of Tasks)
    • 20:25 - Preparing the Data (Validation and Training Set)
    • 22:15 - Using Vgg16 (Finetune and Train)
    • 22:48 - Submitting to Kaggle
    • 30:30 - Competition Evaluation Metric: Log Loss
    • 37:18 - Experiment: Running More Epochs
    • 40:37 - Visualizing Results
  • 47:37 - Introducing the Kaggle State Farm Competition
    • 50:29 - Question: Will ImageNet Finetuning Approach work for CT Scans?
  • 53:10 - Lesson 0 Video, Convolutions
  • 54:09 - Why do we do finetuning?
  • 54:43 - What do CNNs learn?
  • 1:03:30 - Deep Neural Network in Excel
  • 1:14:08 Linear Model from Scratch
    • 1:15:10 - Loss function
    • 1:15:49 - Update function
    • 1:24:40 Question: What if you don't know derivative of functions?
  • 1:25:37 Linear Model in Keras
  • 1:29:58 Linear Model with CNN Features for Dogs Vs Cats Redux
  • 1:44:12 Introducing Activation Functions
  • 1:46:51 Universal Approximation Theorem
  • 1:48:20 Review: Vgg16 Finetuning