Capstone – Reading a Training Run: the Balance Point, Spread, Occupied Bins and Mode Collapse
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Lessons in this course
- The Compression Idea: 2 × 2 Toy Images, an Encoder and a Two-Value Bottleneck Pro
- Decoding and Reconstruction Error: Sigmoid Outputs and Squared Errors Pro
- One Training Step: Gradients for the Decoder Weights, a Learning Rate and the Loss Before and After Pro
- Anomaly Scores: Reconstruction Errors, a Percentile Threshold, Precision and Recall Pro
- Latent Space: Codes on a Chart, Interpolating Between Images and Denoising Pro
- Variational Autoencoders: Mean and Log-Variance, Sampling with Given ε, the KL Term and β Pro
- GANs – the Two Players: Discriminator Scores, Discriminator Loss and Generator Loss Pro
- Capstone – Reading a Training Run: the Balance Point, Spread, Occupied Bins and Mode Collapse Pro