🔥 Classic DeepLearning Models by Jax

On MNIST: (a) acc[96.80%] & loss vs. epochs for mlp; (b) acc[98.24%] & loss vs. epochs for LeNet

# models implemented in this project

• Linear Regression
  • Self Made Gauss-Noise of a Function.
• Logistic Regression
  • Iris.
• KNN
  • CIFAR-10.
• MLP
  • MNIST.
  • CIFAR-10.
• LeNet[1]
  • MNIST.
  • CIFAR-10.
• LSTM
  • UCI HAR.
• GRU[2]
  • UCI HAR.
• Transformer[3]
  • WMT15. TODO
• Nerual ODE[4]
  • MNIST. TODO
• VAE[5]
  • MNIST.

# Plugins

• Minitorch, branch: main.
• LrKit, branch: jax.

@ Number of Codes

Last update: 2025.03.14.

     236 text files.
     135 unique files.                              
     138 files ignored.

github.com/AlDanial/cloc v 1.98  T=0.05 s (2810.5 files/s, 307803.1 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
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Python                          33           1689           3297           3177
Jupyter Notebook                21              0           3947           1913
Text                             6              1              0            301
CSV                             68              0              0            203
Markdown                         5             40              0            198
TOML                             2              3              0             16
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SUM:                           135           1733           7244           5808
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Reference

[1] LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., & Jackel, L. (1989). Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1(4), 541–551.
[2] Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the Difficulty of Training Recurrent Neural Networks. In Proceedings of the 30th International Conference on Machine Learning (ICML) (pp. 1310–1318).
[3] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, ., & Polosukhin, I. (2017). Attention is All You Need. In Advances in Neural Information Processing Systems (NeurIPS).
[4] Chen, T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018). Neural Ordinary Differential Equations. In Advances in Neural Information Processing Systems (NeurIPS).
[5] Kingma, D., & Welling, M. (2014). Auto-Encoding Variational Bayes. In International Conference on Learning Representations (ICLR).