Bibliography (15):

  1. Deep Double Descent: Where Bigger Models and More Data Hurt

  2. Deep Residual Learning for Image Recognition

  3. Understanding ‘Deep Double Descent’

  4. Rethinking Bias-Variance Trade-off for Generalization of Neural Networks

  5. https://x.com/francoisfleuret/status/1269301689095503872

  6. https://arxiv.org/pdf/2105.14368.pdf#page=18

  7. Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited

  8. https://windowsontheory.org/2019/12/05/deep-double-descent/

  9. Reconciling modern machine learning practice and the bias-variance trade-off

  10. CIFAR-10 and CIFAR-100 Datasets

  11. Wikipedia Bibliography:

    1. Convolutional neural network

    2. https://en.wikipedia.org/wiki/Early_stopping  :

      https://en.wikipedia.org/wiki/Early_stopping

    3. Smoothing spline

    4. Stochastic gradient descent

    5. CIFAR-10