Typos and proposals

[hasebe]

Typos

• All notebooks
  • Note: This notebook is desinged -> Note: This notebook is designed
• Chapter02/1. Logistic regression model.ipynb
  • [LRM-07] - Generate inital parameter values. -> Generate initial parameter values.
  • [LRM-10] - jax.vlue_and_grad() -> jax.value_and_grad()
• Chapter02/3. MNIST softmax estimation.ipynb
  • [MSE-06] - Generate inital parameter values. -> Generate initial parameter values.
• Chapter03/1. Single layer network example.ipynb
  • [SLN-05] - Generate inital parameter values. -> Generate initial parameter values.
• Chapter03/2. MNIST single layer network.ipynb
  • [MSL-06] - Generate inital parameter values. -> Generate initial parameter values.
• Chapter03/3. Hyper parameter tuning.ipynb
  • [HPT-10] - Train models with different number of nodes, and record the reults. -> Train models with different number of nodes, and record the results.
• Chapter03/4. Double layer network example.ipynb
  • [DLN-05] - Generate inital parameter values. -> Generate initial parameter values.
• Chapter05/3. CNN interpretation - image generation.ipynb
  • [CII-08] - Defnine a dual layer CNN model. -> Define a dual layer CNN model.
• Chapter05/5. Transfer Learning with ResNet.ipynb
  • [TLR-23] - Show prediction reults for sample images. -> Show prediction results for sample images.
• Chapter05/6. MNIST autoencoder example.ipynb
  • [MAE-07] - Define a loss function as the mean squar error. -> Define a loss function as the mean square error.
• Chapter05/7. MNIST DCGAN example.ipynb
  • [MDE-08] - Define a loss funcion to train the generator. -> Define a loss function to train the generator.
  • [MDE-09] - Define a loss funcion to train the generator. -> Define a loss function to train the generator.

Need to update to align with source code?

• Chapter01/2. Least squares method with native JAX functions.ipynb
  • [LSJ-04] - (x=0, ..., 12) -> (x=1, ..., 12)
• Chapter01/3. Least squares method with Flax and Optax.ipynb
  • [LSF-04] - (x=0, ..., 12) -> (x=1, ..., 12)

[enakai00]

Thanks for the thorough check!