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Build a 2D convolutional neural network, part 5: Pre-trained model results
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Classroom Contents
Convolution in Two Dimensions
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- 1 How Convolution Works
- 2 The Softmax neural network layer
- 3 Batch normalization
- 4 Build a 2D convolutional neural network, part 1: Getting started
- 5 Build a 2D convolutional neural network, part 2: Overview
- 6 Build a 2D convolutional neural network, part 3: MNIST digits
- 7 Build a 2D convolutional neural network, part 4: Model overview
- 8 Build a 2D convolutional neural network, part 5: Pre-trained model results
- 9 Build a 2D convolutional neural network, part 6: Examples of successes and failures
- 10 Build a 2D convolutional neural network, part 7: Why Cottonwood?
- 11 Build a 2D convolutional neural network, part 8: Training code setup
- 12 Build a 2D convolutional neural network, part 9: Adding layers
- 13 Build a 2D convolutional neural network, part 10: Connecting layers
- 14 Build a 2D convolutional neural network, part 11: The training loop
- 15 Build a 2D convolutional neural network, part 12: Testing loop
- 16 Build a 2D convolutional neural network, part 13: Loss history and text summary
- 17 Build a 2D convolutional neural network, part 14: Collecting examples
- 18 Build a 2D convolutional neural network, part 15: Rendering examples
- 19 Build a 2D convolutional neural network, part 16: Cottonwood code tour
- 20 Build a 2D convolutional neural network, part 17: Cottonwood cheatsheet
